A systematic study of double auction mechanisms in cloud computing

A systematic study of double auction mechanisms in cloud computing

Accepted Manuscript A Systematic Study of Double Auction Mechanisms in Cloud Computing Dinesh Kumar , Gaurav Baranwal , Zahid Raza , Deo Prakash Vidy...

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Accepted Manuscript

A Systematic Study of Double Auction Mechanisms in Cloud Computing Dinesh Kumar , Gaurav Baranwal , Zahid Raza , Deo Prakash Vidyarthi PII: DOI: Reference:

S0164-1212(16)30254-0 10.1016/j.jss.2016.12.009 JSS 9896

To appear in:

The Journal of Systems & Software

Received date: Revised date: Accepted date:

9 March 2016 28 September 2016 10 December 2016

Please cite this article as: Dinesh Kumar , Gaurav Baranwal , Zahid Raza , Deo Prakash Vidyarthi , A Systematic Study of Double Auction Mechanisms in Cloud Computing , The Journal of Systems & Software (2016), doi: 10.1016/j.jss.2016.12.009

This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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Highlights A detailed study of the double auction mechanisms in cloud is provided.



A framework for double auction in cloud is proposed for a future cloud market.



A model TMDA is proposed to infer how a truthful double auction can be designed.



TMDA is asymptotically efficient, individual rational, truthful and budget-balanced.



Various challenges and future scope in double auction in cloud are also presented.

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A Systematic Study of Double Auction Mechanisms in Cloud Computing

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Department of Computer Science and Engineering, Madan Mohan Malaviya University of Technology, Gorakhpur, Uttar Pradesh, India

[email protected], [email protected], [email protected], 4 [email protected]

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School of Computer and Systems Sciences, Jawaharlal Nehru University, New Delhi, India

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Dinesh Kumar1, Gaurav Baranwal2, Zahid Raza3, Deo Prakash Vidyarthi4

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Corresponding author: Dinesh Kumar, School of Computer and Systems Sciences, Jawaharlal Nehru University, New Delhi-110067, India, Ph. +91 11 26717676

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Email- [email protected]

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Abstract

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The cloud system is designed, implemented and conceptualized as a marketplace where resources are traded. This demands efficient allocation of resources to benefit both the cloud users and the cloud service providers. Accordingly, market based resource allocation models for cloud computing have been proposed that apply economy based approaches e.g. auction, negotiation etc. This work makes a detailed study of the double auction mechanisms and their applicability for the cloud markets. A framework for a future cloud market using double auction is also proposed. As most of the existing works in double auction confines only resource allocation, therefore, a Truthful Multi-Unit Double Auction mechanism (TMDA) is proposed that would help researchers to understand how a truthful double auction mechanism can be designed. TMDA is proven to be asymptotically efficient, individual rational, truthful and budget-balanced. TMDA would also encourage researchers to contribute in this emerging area. The performance of TMDA, which addresses the interests of both the cloud user and the provider, has been validated through simulation study. Various challenges in the realization of double auction mechanisms in cloud computing along-with the future possibilities are also presented. Keywords: Cloud computing, Double auction, Resource allocation, Mechanism design, Quality of Service (QoS)

1 Introduction

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Recently, a tremendous growth has been observed in the adoption of cloud computing by various organizations owing to various technological advancements and the appealing business centric nature of the cloud. Cloud computing is emerging as an important utility and is growing rapidly well supported by the major service providers e.g. Amazon, Microsoft, IBM, Google to name a few. The paradigm is successful with the establishment of various interoperability, security and QoS standards coming in place (Sajid and Raza, 2013). The main goal of cloud computing is to deliver storage, network, servers, computing or their combination as a service to the users (Buyya et al., 2013). NIST defines five basic characteristics of cloud computing as: on demand self-service, broad network access, resource pooling, elasticity and measured service (Snaith et al., 2011). On demand provisioning of services and pay per use model has become the main features offering big advantage of cloud computing (Garg et al., 2013).

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The growing popularity of cloud computing services demands attention on the business aspects of the cloud for the resource provisioning and pricing issues. In a cloud market, various cloud providers want to sell their resources amongst multiple potential cloud users. The concept of the cloud market is still new and is different from other conventional markets in several aspects such as resource pricing mechanisms, service provisioning strategies, resource allocation rules and payment mechanisms. In a cloud market, a cloud provider competes with other resource providers with respect to expected pricing and QoS in order to maximize its Return on Investment (ROI). On the other hand, cloud users compete with each other for the required (limited) resources in least pricing and good QoS minimizing the total procurement cost. Thus, a cloud user is interested to benefit all the inherent features of the cloud viz. scalability, elasticity, flexibility, disaster recovery and at the same time, the cloud service providers vies to maximize their profit. A major challenge in such a cloud system thus becomes to find an optimal market 3

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based on unbiased strategy and offering the best possible satisfaction to both the cloud provider and the cloud user.

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Game theory and mechanism design (Narahari, 2014) provides a basic framework to design and study such competitive markets. Also, the market strategy that is unbiased for both the user and the provider is highly desirable. As both the user and the provider are rational, intelligent and competing in the cloud market, auction based models are quite useful to model in such types of situation (Samimi et al., 2015). In auction, the price is determined by the supply and demand of the resources. Auctions are easy to implement, decentralized and suitable for distributed systems e.g. grid computing, cloud computing etc. (Buyya et al., 2002). It is also one of the many ways to implement the dynamic pricing. Dynamic pricing mechanism is desirable in cloud market because of its various advantages over the fixed pricing strategy. Dynamic pricing increases the total revenue of the resource provider by frequently changing its selling price which depends upon the supply/demand of the resources and many other factors (Narahari et al., 2005). It also creates the healthy competition among the users and increases the efficiency of the cloud resource usage (Mihailescu and Teo, 2010). A real example of such dynamic pricing implementation in cloud systems is Amazon’s Spot market (“Amazon EC2 Spot Instances”) an auction based cloud market. Amazon sells its spare capacity as spot instances without disclosing any information about the auction mechanism and the spot price mechanism. In recent, Google has also initiated offering cloud services based on the dynamic pricing (“Google : Dynamic Pricing” ).

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Mainly, there are three types of auction: Forward, Reverse and Double auction based on the bidding structure. In forward auction, multiple buyers compete with each other by bidding for the resources offered by a single cloud provider. Reverse auction occurs between a single cloud user and multiple cloud provider. The user first reveals its call for proposal (CFP) by quoting the required resources. Multiple cloud providers then compete with each other by bidding for the resources required by this single user. In double auction bidding is done by both the players of the market i.e. multiple buyers and the multiple sellers. Till now, in the real cloud market, only forward auction has been used by Amazon to sell the underutilized spot instances (“Amazon EC2 Spot Instances”). However, in literature, various models based on all types of auctions are reported. This work focuses only on double auction mechanisms in cloud computing.

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Researchers and professionals have studied the double auction mechanisms for cloud computing, as summarized in this work, but till now no real implementation of double auction has been done in a real cloud market. Though, some laboratory e-market supports the double auction mechanism (Wellman et al., 2001) but the auction properties are not guaranteed. In future, it is quite likely that companies and business world will start offering cloud services and cloud users will avail these services. A good competition is expected between cloud users and service providers. Various benefits of double auctions includes dynamic pricing, efficient resource allocation, supply and demand principles, less time consumption (Narahari et al., 2005). To the author’s best knowledge, a double auction model for cloud computing market that is truthful for all the participants i.e. users as well as providers has not been proposed till date. Most of the reported work ensures the truthfulness for either the cloud users or cloud providers. This work presents an exhaustive study of the basic double auction mechanisms, their properties and their applicability with a detailed discussion in context to the cloud computing. Salient features of the proposed work are as follows: 4

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Most of the double auction mechanisms, proposed for cloud computing in the past, are detailed and compared in terms of various basic auction properties. A general framework for double auction in cloud market is designed which gives a direction to the researchers and business professionals to design double auction mechanisms for cloud computing environments. A multi-unit double auction based mechanism (TMDA) is proposed for the cloud computing market. TMDA is individual rational, budget-balance along with being truthful for both the cloud users and the providers. Various challenges of double auction mechanisms are discussed setting a future direction to explore and extend the double auction mechanisms for the cloud systems.

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The rest of the paper is as follows. A thorough survey on the double auction mechanisms in cloud computing has been done in section 2. In section 3, a framework for double auction in the cloud market is presented followed by a truthful multi-unit double auction (TMDA) model for the cloud resource allocation in section 4. Section 5 discusses various issues and challenges for the double auction based cloud computing market. Section 6 concludes the work.

2 Double Auction

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(McMillan, 1994) documented the Federal Communications Commission (FCC) auction held in 1994 for selling the spectrum using auction which generated interest among researchers and scholars too working in the domain of auction. Another reason for bolstering the growth of auction based approaches is due to explosion of B2B exchanges. For example, a special application of auction is the procurement of complementary goods. Forrester has estimated that in 2003 (www.forrester.com), B2B trade value exceeded $1.5 trillion of which $650 billion trade was through auction. Auction results in the implementation of the dynamic pricing which is very important in such a scenario along-with several other advantages e.g. decentralized control, dynamic resource management, revenue maximization and effective priority allocation to different QoS attributes (Parsons et al., 2011).

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Many of the competitive markets e.g. bonds, stocks and commodities, internet markets where trading or exchanges of goods or resources happen among market participants, can be modelled using large double auctions with incomplete information (Bratton et al., 1982). Double auction is based on the supply and demand principle. After submission of bids from both the sides, the auctioneer sets a final trade price at which supply equals the demand. This price is called the equilibrium price.

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2.1 Double Auction Properties Any mechanism design is expected to exhibit certain properties to qualify in a certain group as established in the literature. Accordingly, the design of a double auction mechanism should fulfill the following desirable properties. Economic Efficiency While applying the double auction mechanisms, main focus of the mechanism designer is on efficient auctions. Efficiency, in the context of double auction, has been explained in many ways 5

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in the literature. One such notion of efficiency is the allocative efficiency which considers the performance of the entire system rather than the individual profit. Given the bids information of all the participants (providers and customers), auctioneer can easily calculate the total profit or surplus (sum of valuation of all participants) in the mechanism. Allocative efficiency, , can be defined as the ratio of the actual total profit of the auction participants and the total profit that can be obtained theoretically .

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⁄ A 100% efficient double auction mechanism indicates to generate maximum possible profit or surplus. In other words, an efficient double auction maximizes the total profit obtained by all the participants. Sometimes, efficiency is measured in terms of social welfare. A mechanism that maximizes the social welfare (sum of valuations or sum of utilities of all agents) is considered efficient if the resources are allocated to the customers who value them the most and the resource are offered by the providers who offers the resources in least price with better QoS.

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Individual rationality

A double auction mechanism is said to be individual rational if the utility of all the participants is always positive i.e. participants are not at a loss by participating in the auction. Conceptually, this property corresponds to motivate a participant to take part in the auction by assuring nonnegative utility.

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Budget balance

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Budget balance is a strong requirement and should be fulfilled while designing the double auction mechanisms. Normally, this property is not significant in single-sided auction. Conceptually, it provides the motivation to the market maker or the auctioneer to hold the auction. In double auction, budget balance requires that the total charges from the customers cannot exceed the total payments to the providers. If the sum of the payment of all the customers is equal to the sum of received payment of all the providers, the mechanism is said to be exactly budget balanced. If the difference between the sums of payments is greater than zero, it is called weakly budget-balance. Computational Efficiency

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This property represents the computational perspective of the auction mechanism. This indicates that the auction mechanism, consisting of the allocation and payment mechanism, should run in polynomial time. Incentive Compatibility A double auction is said to be incentive compatible or truthful if truthful bidding is the dominant strategy of each bidder i.e. no participant can gain in its utility by bidding untruthfully. In other words, no provider can gain profit by bidding value other than its actual valuation and no customer can gain in profit by bidding untruthfully. This property is achieved by designing

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specific type of payments between the participants (Narahari, Y.; Garg, D.; Narayanam, R.; Prakash, 2009). (Lehmann and O’Callaghan, 2002) detailed the four properties of single minded bidders that should be satisfied for truthfulness: Exactness, Monotonicity, Critical Payment, and Participation. If the above four properties are present in a mechanism, the mechanism is incentive compatible.

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Incentive compatibility can further be categorized into two categories: 1. Dominant Strategy Incentive Compatibility: Each bidder achieves maximum utility when it reveals its private information truthfully no matter how the other participants are bidding. 2. Bayesian-Nash Incentive Compatibility: Each bidder bids truthfully only when all other bidders are also revealing truths. This is a weaker form of Incentive compatibility.

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Vickrey-Clarke-Groves (VCG) payment scheme (Nisan and Ronen, 2007) is the most efficient among all available truthful mechanisms. In Bayesian settings, the VCG scheme results in optimum revenue with ask price. Fairness

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A double auction mechanism should be fair for all the participants i.e. customers and providers. Fairness, in an auction mechanism, can be implemented in many ways such as priority and reservation price (Murillo et al., 2012). Fairness reduces the bidder drop problem and has long term benefits for an auction market (Murillo et al., 2011) (Lee and Szymanski, 2005). The approach used to achieve fairness is also called egalitarian based approach and the social welfare generated from such fair markets is called egalitarian social welfare.

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The design of a truthful mechanism depends upon the nature of the bid attribute. A bid attribute can be categorized into verifiable and non-verifiable attributes (Pla et al., 2014) e.g. price is a non-verifiable attribute because it is known only by the provider itself and cannot be checked by any other participant due to its subjectiveness. On the other hand, QoS and quantity of offered resources comes into the category of verifiable attributes i.e. values of these attributes can be verified after the allocation of the resources. Therefore, another approach to hold the spirit of the market and to avoid its wrong manipulation is egalitarian based approach. Fairness based criteria is used during the allocation with the provision of penalty on provider for false disclosure or when it is not able to meet the quoted QoS (Baranwal and Vidyarthi, 2015).

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Though, it is desired to adhere all properties while designing a double auction mechanism often it is impossible to do so (Myerson and Satterthwaite, 1983). Of the properties, budget balance and individual rationality are necessary for the sustainable auction i.e. bidders will not take part in auction voluntarily if they suffer a loss by participation and the auctioneer will not perform the auction in a longer run if the mechanism does not satisfy the budget balance property. For complex scheduling situations such as combinatorial double auction, where bidders bid in the form of bundles, economic efficiency and computational efficiency conflict with each other. To fully understand the double auction mechanisms for different environments and settings, in the next few sections, basic double auction mechanisms and their applicability in the market is discussed. In the next section, various variants of double auction from a mechanism design 7

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perspective and various auction properties required in auction is discussed. A comparative study of all truthful double auction mechanisms in terms of basic auction properties is done. After that, a literature survey of double auction mechanisms in cloud computing is carried out which compares all existing works in terms of auction properties as well as their research objectives in the model, drawbacks, design environment, benchmark, evaluation method etc. 2.2 Applicability of Double auction in Cloud Computing

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As discussed before, in the real cloud market, only forward auction has been used by Amazon to sell the underutilized spot instance (Amazon Web Services EC2, 2014). However, in literature, various models based on all types of auctions are reported. Currently double auction in cloud is futuristic scenario and may be based on certain assumptions like request indivisibility, interoperability etc. So far, cloud market based on double auction does not exist though the possibility is potentially good. Though various works based on double auction has been reported in literature that proves applicability of double auction in cloud, here in this section it has been explained in order to provide completeness of this work.

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In the recent past, availability of multiple Cloud service providers in the Cloud market resulted in multi-cloud (Petcu, 2013), Cloud federation (Kurze et al., 2011) etc. Multi-cloud is presence of multiple Cloud providers in the market and customer can select a best service among them or can take services from more than one provider while federation is cooperation among service providers to maximize the satisfaction of customer. Federation is also possible in multi-cloud. In literature, the term Inter-cloud is used to cover all aspects of the scenario of availability of multiple Cloud providers.(Bernstein et al., 2009) defines inter-cloud as “Cloud of Clouds” i.e. inter-cloud is a unified mesh of Cloud based on open standards to provide interoperability. There are some evidences that foresee the implementation of inter-cloud (Multi-cloud, federated cloud etc.) in near future. It is because there are so many advantages of inter-cloud to both; provider and customer such as Better Services even in Peak Times, Better QoS Deliverance to Customer, Cost Efficiency and less Energy Consumption etc. (Pardo, Jorge ; Flavin, Andrew ; Rose, 2016) provides a Cloud computing market report after a rigorous study of Cloud market in various countries. It finds that 17 of top 20 Cloud service providers are based in United States while Japan is the top market for Cloud computing export. Most of the Cloud service providers are from non-European countries. Cloud service providers from the European countries are striving to attract Cloud customers and inter-cloud is a possible solution for the better services. Therefore, European Commission has funded a good number of projects to deal inter-cloud such as MODAClouds (ModaClouds, 2015), TClouds (Tclouds-project, 2016), REMICS (REMICS, 2016), Cloud4SOA (Cloud4SOA, 2016) etc. There are some other on-going and implemented research projects by professionals and researchers from the other countries too. (Grozev and Buyya, 2014) listed some projects belonging to inter-cloud. Cloud Computing Interoperability Forum (CCIF), DMTF Cloud Standards Incubator, Open Cloud Manifesto etc. are some organizations that are trying to make some open standards. Open Virtualization Format (OVF), supported by few Cloud vendors, provides facility to switch from one vendor to another (Toosi et al., 2014) Availability of a large number of service providers made the Cloud market a competitive place and is the main cause for implementation of dynamic pricing by providers to attract customers. There is various new business or companies that have started to offer the cloud services. This leads to a cloud market where cloud users and cloud providers interact with each other for trading the cloud resources (IaaS, PaaS or SaaS resources) (Chichin et al., 2015) (Feng et al., 8

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2014). In this market, users compete with each other for getting the limited cloud resources at optimal cost. Along this, all cloud providers compete with each other for offering their cloud services for maximizing their own profit or revenue. This reveals the presence of both-side competition in the market i.e. user as well as provider side. Double auction mechanisms provide a concrete and suitable framework for modelling the both side completion of an auction based cloud market. In double auction, as bidding happens on both sides of market, preferences of both the users and providers are considered in the whole resource allocation and pricing mechanisms. In addition, use of double auction instead of repeated single-sided auction reduces the computational burden and complexity on the seller/provider side. One-sided auctions e.g. procurement auctions are more suitable and easy to use when there are less number of cloud users. But, when number of users increases, computational burden also increases on provider side due to repeated bidding behavior. The whole process is also time taking as all the possible auction outcomes are contemplated by the provider. This reduces the possible trades or transactions, especially in combinatorial auctions (Xia et al., 2005).

2.3 Literature Survey on Double Auction

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Double auction is a many-to-many auction that prevents monopoly and can be used to design an unbiased optimal market strategy for cloud market (Parkes et al., 2001). It is more complex in implementation but considerably more efficient than several single one-sided combined auction (Parkes et al., 2001). In fact, double auctions are highly efficient when the number of participants are large in numbers (Cason and Friedman, 1996) (Plott, 1990). In addition, it is noticed that double auction yields more revenue compared to the single sided auction in the long run (Wise and Morrison, 2000).

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Double auction is a basic mechanism for two-sided trading i.e. from the customer and the provider. A literature survey has been done on double auction as follows.

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2.3.1 Truthfulness in Auction Mechanisms

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The credit for development of market based truthful (strategy-proof) mechanisms goes to Vickrey, Clarke and Groves. These mechanisms are widely known as VCG mechanism. Groves (Groves, 1973) and Clarke (Clarke, 1971) proved that a mechanism is both allocative efficient and strategy-proof if the utility function are quasi-linear. Groves’ mechanisms are the most generalized ones in quasi-linear environments. Clarke’s mechanism is a special class of Groves’ mechanisms. Generalized Vickrey Auction (GVA) (Varian, 1995) is the combinatorial auction version of Clarke’s mechanisms. Vickrey Auction (Vickrey, 1961) is the non-combinatorial version of GVA i.e. the second price sealed bid auction of a single individual item.

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2.3.2 Related work on Basic Double Auction Mechanisms Some works, applying double auction mechanism, are as follows. (Hurwicz, 1977) showed that it is impossible to implement an efficient, budget balanced and strategy-proof mechanism in simple exchange environment. (Myerson and Satterthwaite 1983) further improved the results of (Hurwicz, 1977) showing that as long as a mechanism is individual rational, it is impossible to achieve both the incentive compatibility and budgetbalance with an efficient outcome i.e. an efficient mechanism with incentive compatibility always lead to budget deficit as long as individual rationality is considered. Keeping this in 9

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mind, several Incentive Compatible (IC) double auction mechanisms have been designed that achieve budget-balance by sacrificing the efficiency (Parkes et al., 2001) and in turn being suboptimal allocation. One way to design a simple double auction mechanism is by Trade Reduction (TR) method (Chu and Shen, 2006). In this method, the double auction comprises of two phases viz. allocation phase and the pricing phase. Both the phases are designed in such a way that it satisfies individual rationality, weak budget balance and incentive compatibility. During allocation, it rejects the least profitable trade and selects the remaining trades. The bids, rejected in allocation become the reference prices for the pricing mechanism for calculation of the final trade prices. Another way to design the double auction mechanism is by Multi-Stage approach (MS) (Chu and Shen, 2007). In multi-stage design approach, efficient allocation is done in multiple stages and partial decision process happens for one side of market say buyer side. Threshold prices are calculated for removing some unqualified buyers, i.e. those buyers whose bid prices are more than threshold prices are allowed in final allocation process. Those qualifies buyers are then matched with original sellers for most efficient allocation.

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(McAfee, 1992) designed a strategy-proof, budget balanced and an efficient double auction mechanism by extending the Trade Reduction (TR) method for the simple exchange environment in which the sellers are selling unit capacity of each item. (Babaioff and Nisan, 2004) proposed two randomized and normalized double auction mechanisms named “The Reduction DA” and “The payment DA” for concurrent supply chain problem by considering the tradeoff between budget balance and auction efficiency. These mechanisms achieve higher efficiency than TR method (when taken expectation over allocation and payments results and bids are generated independently by uniform distribution ). Tradeoff between the budget-balance and auction efficiency is controlled by a parameter . Exact budget balance in Expectation can also be achieved in the mechanism if is chosen very carefully. In “The Reduction DA” mechanism, after bid submission, TR method is used with probability and VCG method is used with probability . “The payment DA” is an individual rational and incentive compatible double auction mechanism but not a universally incentive compatible randomized double auction mechanism.

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Huang et al. (Huang et al., 2002) generalizes the work of (McAfee, 1992) by designing a mechanism for multi-unit exchange environment that is strategy-proof, budget balanced and individual rational. In this work, a subset of trades is selected by rejecting the least efficient trade (buyer with lowest bid and provider with highest ask). In case, if there is an over demand or over supply of commodities or goods, allocation is done by averaging all the over demand to first buyers and over supply to first sellers where and are the number of winning buyers and sellers who successfully trade in double auction mechanism respectively. (Babaioff and Walsh, 2005) extended the TR technique for combinatorial/single unit settings where buyers bid in the form of bundle and each seller sells a single unit of each commodity. They considered the case when the requested bundle information is in common knowledge as well as the case in which this bundle information is private. Incentive Compatible (IC) double auction mechanisms was designed with TR allocation by removing a subset of trades from the efficient allocation and payments with highest and lowest bids that also satisfies budget-balance and individual rationality. The experimental and theoretical results show that the mechanisms are more efficient (asymptotic) as compared to the other IC mechanisms. (Chu and Shen, 2006) 10

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proposed truthful and multi-stage double auction mechanisms for customer-to-customer market place. The mechanisms proposed are individual rational, incentive compatible, budget-balanced and asymptotic efficient. They considered the bundle/single-unit environment where a customer bid for bundle of commodities and a producer or seller sell a single unit of single commodity. They considered the transactional cost of the transaction and the mechanism proposed generates higher social welfare and revenue than other standard techniques. In another work, (Chu and Shen, 2007) compared the two standard approaches of double auction designing i.e. TR and MS and discussed the applicability of these two mechanisms in different environments. These two methods of designing the double auction mechanisms are then compared in terms of social welfare and individual payoffs. The work insists that MS approach is more suitable for most of the market types as compared to the trade reduction approach.

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(Chu and Shen, 2008) further proposed two incentive compatible double auction BC-LP (Buyer Competition - Linear programing) and MBC (Modified Buyer Competition) mechanisms based on multi-stage approach. They considered an exchange environment where a buyer procures the commodities in form of bundle with seller selling a unit capacity of a single type of commodity. Both the mechanisms, designed here, used the multi-stage approach and satisfies the budgetbalance and incentive compatibility. These mechanisms are compared with the KSM-TR (known as single-minded trade reduction) mechanisms (Babaioff and Walsh, 2005) in terms of social welfare and average efficiency. The mechanisms proposed in (Chu and Shen, 2008) assumed that each seller is allowed to sell a single unit of single type of commodity. When this constraint is relaxed, these mechanisms lost the budget-balance property. Accordingly, these mechanisms cannot be used in multi-unit environments. (Chu, 2009) extend the work of (Chu and Shen, 2008) and proposed an incentive compatible, budget-balanced and asymptotic efficient double auction mechanisms using a novel padding method for combinatorial/multi-unit environment. These mechanisms have shown to be more efficient than the bundle/single-unit mechanisms in terms of selling and buying prices.

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The comparison of the above discussed double auction methods, based on the double auction properties, is presented in Table 1. Table 1: A Comparison of Double Auction Mechanisms

Dimension (User) / Dimension (Provider) Single-Unit/Single-Unit Single-Unit/Single-Unit Multi-Unit/Multi-Unit Combinatorial/Single-Unit Combinatorial/Single-Unit Combinatorial/Single-Unit Combinatorial/Multi-Unit, MultiItem Economic Efficiency (EE), Individual Rational (IR), Incentive Compatibility (IC), Budget-Balance (BB)

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Basic Truthful Double Mechanisms in Literature VCG (McAfee, 1992) (Huang et al., 2002) (Babaioff and Walsh, 2005) (Chu and Shen, 2006) (Chu and Shen, 2008) (Chu, 2009)

Auction

Auction Properties EE IC IR BB                            

2.3.3 Related Work on Double Auction Mechanisms in Cloud Computing Some works, applying double auction mechanism for the cloud resources, are as follows.

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(L. Wang et al., 2014) presented a very basic and simple cloud resource management framework for cloud resource trading. Trading prices were calculated using double auction method and resource allocation problem was solved using Cell Membrane Optimization (CMO) technique. Bid values of Cloud Resource Consumer (CRC) and Cloud Resource Provider (CRP) updates continuously in the successive rounds of auction. A CRC bid value increases if buyer fails to get its requested resources while CRP bid value decreases if it fails in selling its resources. In each round, trading price is calculated by taking the average of bid prices of user and provider. In other cases, the bid values remain fixed. After a fix number of iterations, auction process stops. After finding the winning CRC and CRP in double auction phase, resources are allocated using the CMO method and optimization values introduced in objective function of the proposed method. Three evaluation criteria i.e. close rate, users' profile rate and trading price satisfaction are used for estimating the resource allocation scheme. The effectiveness of the resource allocation scheme is proved by simulation in CloudSim (Calheiros et al., 2011). However, the complexity and the nature of the resources in cloud market are not considered in the proposed schemes and auction properties for the proposed pricing scheme were also not discussed.

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(Shang et al., 2010) proposed a uniform, flexible and competitive framework for cloud market in which users and providers interact with each other for buying and selling the cloud resources. Besides, an optimal pricing strategy based on double auction and Bayesian game was also reported for the cloud market. Although the proposed model can encourage both the user and the provider to participate in the cloud market for resource trading, it lacks the performance evaluation and analysis of the allocation and pricing strategy. Also, an appropriate simulation study to support the claims, is not present for this model.

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An economically efficient marketplace is designed for the cloud computing environment using double auction mechanism by (Fujiwara et al., 2010). Two markets i.e. forward and spot markets are considered for the future requests and immediate requests respectively. The service allocation problem is solved using the mixed-integer programming (MIP) technique and evaluated in terms of the computation time. The scalability and flexibility of the model is shown using simulated experiment. However, the model is not incentive compatible and is proposed only for arbitrary combination of services. The model lacks detailed analysis and does not work when cloud market size increases.

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Combinatorial double auction is proposed for the resource allocation and pricing in grid systems (LI et al., 2009). The proposed method takes into account both the user and provider preferences and calculates the final trade price. Though, the work claims to be incentive compatible through the experimental studies, it uses the average price mechanism for payment schemes in double auction mechanism which is theoretically not an incentive compatible mechanism. A continuous double auction (CDA) based Grid resource allocation model proposed in (Izakian et al., 2010) where Grid users request resources to execute their jobs. The users determine their bid values depending on the remaining resources and average remaining time for bidding the resources. As the number of remaining resources or the mean remaining time of the bidding decreases, the bid value of grid user increases. Depending upon these two parameters, user’s final bid value is evaluated. The bid value of the grid provider is also fluctuating between its maximum price and the ask price depending upon the workload. After finding both the bid value, both the user and the provider trade at price average of the highest bid and the lowest request. 12

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Simulation results prove the effectiveness of auction mechanism in terms of fairness deviation, resource utilization and mean trade price which are intensive for both grid user and the provider.

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(Farajian and Zamanifar, 2013) proposed a market-driven continuous double auction inspired from the wok proposed in (Izakian et al., 2010). Similar to (Izakian et al., 2010), users can adjust their bids and find acceptable market prices by considering various factors such as time, competition, opportunity, eagerness. Providers adjust their ask prices in order to maximize profit and increasing resource utilization. Providers consider time and opportunity factor while adjusting their bids. After converging to final acceptable market price, an average of values of matching user and provider is taken which is not truthful. For simulation, a computational cloud simulator JADE (Java Agent Development Framework) is used. The proposed method is compared with three other continuous double auction based models in terms of average resource utilization, trading price, and success rate. The simulation study shows that the method performs better than its peers. However, the model adopts the continuous form of double auction which is different from the proposed approach and is also not truthful for any participant.

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A similar work has been proposed in cloud computing by (Samimi et al., 2015). They proposed a combinatorial double auction for resource allocation (CDARA) model in cloud computing by extending two models proposed by (LI et al., 2009) and (Zaman and Grosu, 2013) for efficient allocation and pricing. The obtained allocation solution is proved to be approximately efficient with the pricing mechanisms motivated by (LI et al., 2009). The performance of the proposed model is shown using two evaluation criteria: economic efficiency and incentive compatibility. Average of the bid prices of the matching providers and bidders are taken for the calculation of final trade price which is not incentive compatible analytically.

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(X. Wang et al., 2014) proposed a cloud resource allocation model based on double multiattribute auction (DMAA). In the model, Mean Variance Optimization (MVO) method is used for the resource allocation and Support Vector Machine (SVM) technique is used to predict the resource pricing. In addition, Quality index (QI) values has been derived for all users and providers for their performance evaluation using Neural Network Algorithm (NNA) considering multiple attributes e.g. price and non-price attributes. However, economic efficiency and incentive compatibility issues do not get addressed either analytically or experimentally.

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There are two types of social welfare in mechanism design. One is utilitarian social welfare and another one is egalitarian social welfare. Utilitarian social welfare can be achieved by maximizing the utility of agents in competitive market environment whereas egalitarian social welfare focuses on the fairness in the mechanism i.e. how and upto what extent the mechanism is fair to all the agents in the system. Mostly the reported works in double auction have focused on the utilitarian social welfare while a few of works considered the egalitarian based approach. The literature has focused on egalitarian approach by implementing fairness in the system. A fair multi-attribute combinatorial double auction model (FMCDAM) for resource allocation in cloud computing has been proposed in (Baranwal and Vidyarthi, 2015). FMCDAM considers two models proposed by (Samimi et al., 2015)’ and (Pla et al., 2015) for fair and efficient allocation of cloud resources. The work considers several QoS attributes in addition to the price for the winner determination. The proposed model reduces the bidder drop problem by implementing fairness (Egalitarian social welfare) in double auction based cloud market. In addition, if a provider offers false QoS assurance i.e. offered QoS levels are not met as required, a penalty is imposed and its reputation is decreased which lowers the chance of winning the auction for that 13

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provider in successive rounds. Further, the mechanism makes the system robust by offering compensation to the affected users. However, the mechanism takes the egalitarian approach and satisfies almost all auction properties except incentive compatibility.

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(Wu et al., 2016) proposed two novel economic mechanism named Modified Vickrey Mechanism (MVA) and Continuous Double Auction (CDA) mechanism for scalable and automatic resource allocation in self-organizing cloud. The work mainly focused on designing incentive compatible mechanisms which offer incentives for the providers to reveal their valuations truthfully. The proposed mechanisms enable a cloud user to select an appropriate cloud resource with minimum cost and desired quality. If resources were insufficient, CDA mechanism was used while MVA mechanism was more suitable when resources were sufficient. MVA mechanism enabled a user to run its tasks on the resources provided by the cloud providers at minimum cost while ensuring the incentive compatible property for cloud providers. CDA mechanism was used when resources were insufficient and considered both sided competition. First a winning cloud consumer was selected among all bidding consumers based on their reported prices and budgets. Then the winning consumer executed its tasks and final trade prices were calculated using MVA mechanism. However, the mechanism was not individual rational and not incentive compatible for cloud consumer. Further, QoS parameters were not considered for resource allocation and pricing.

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(Sun et al., 2013) took another approach to tackle the wrong manipulation and malicious bidding behavior of cloud market participants by implementing the feedback rating based reputation system in cloud computing environment. Intelligent agent based double Combinatorial Auction proposed for the resource allocation problem in cloud market. To solve the winner determination problem (WDP) in double combinatorial problem, group search optimization technique has been used. WDP was formulated into the problem of optimizing the allocation matrix (twodimensional variable-length array) representing the resource allocation between cloud customer and cloud provider. After that, a feedback evaluation based reputation system is proposed in which reputation in each step is calculated using feedback evaluation, transaction amount and credibility of participants. Back Propagation Neural Network based pricing is considered which using the historical bidding statistics to train the neural network. Total market surplus, total reputation and time cost were compared on different market scale. A similar work of (Sun et al., 2013) has been proposed by (Wang et al., 2015) with the major difference being in the solving procedure of WDP. (Wang et al., 2015) solves the WDP problem using Paddy Field Algorithm (PFA) wheras (Sun et al., 2013) consider group search optimization to solve the WDP problem. However, the above two models take different approach from ours to handle the wrong manipulation of market. (Xu et al., 2014) designed an efficient and online combinatorial double auction mechanism for resource allocation in Mobile Cloud Computing (MCC) market. First, various bidding languages are proposed for cloud users in order to express their valuations concisely. To address the winner determination problem (WDP), two approaches are used. First one is the exact method by replacing the WDP with a larger feasible region. Second one exploits Artificial Intelligence (AI) searches over all feasible space. Three criterions: The Transaction Volume, The Social Welfare and The Average Ratio of Transaction Prices are used for performance evaluation. The simulation results show that WDP algorithm converges with acceptable iterations and generates solution near to social optimal. 14

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(Prodan et al., 2011) presented a negotiation-based approach for scheduling scientific applications on heterogeneous computing resources in grid and cloud environments. In (Prodan et al., 2011), Continuous Double Auction (CDA) is used for dynamically pricing the resources and managing their access to most eligible users. Authors in (Prodan et al., 2011) detailed the strategies for scheduling workflow applications in open and heterogeneous market. These strategies are in the form of various decision problems such as resource selection, finding resource valuation and designing optimal bidding strategy in auction based markets. For resource valuation, exponential smoothing function is used. Bids are generated in double auction market using zero-intelligence bidding strategy. The proposed scheduling strategy is simulated in GridSim with auction framework.

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(Li et al., 2013) proposed a double auction based inter-cloud VM trading mechanism in federated cloud environment. In the proposed model, each selfish or rational cloud provider intends to maximize its own profit as well as total social welfare in the federated environment. First a multi-unit double auction based inter-cloud VM trading mechanism is proposed which is strategy-proof, individual rational, ex-post budget balanced and achieves near optimal solution. A dynamic job scheduling algorithm is then proposed by scheduling the time varying jobs on different VMs (own as well as leased ones). The algorithm optimally turns on/off the servers based on the electricity pricing. The results show that the algorithm maximizes the profit of an individual cloud provider in an online fashion which is nearly equal to the profit obtained as offline maximum. The algorithm generates more individual profit and social welfare as compared to simple heuristics and outperforms other standard algorithm in terms of average response delay and average dropped jobs.

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(Zheng et al., 2014) proposed two truthful double auction mechanisms in cloud market where cloud providers provide the bandwidth services to the cloud tenants (Multi cloud multi-tenant bandwidth). The first mechanism is a grouping based double auction that groups the cloud tenant irrespective of their bid values and efficiently assigns bandwidths to the cloud tenant. The second mechanism is a strategy-proof double auction that implements a virtual padding tenant demanding unlimited bandwidth. Both the mechanisms proposed are ex-post budget balanced and result shows the effectiveness of the mechanisms in terms of cloud bandwidth reservation, social welfare and tenant satisfaction ratio.

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(Lee et al., 2015) proposed a group auction based framework for cloud instance market. The proposed mechanism considers the combinatorial double auction and cooperation among the auction participants (cloud users as well as providers). A group formation mechanism is proposed for all participants that optimally group the users’ demand and providers’ supply. An allocation mechanism is also proposed that optimally allocates the cloud instances among cloud users. The double auction mechanism proposed is individual rational, allocative efficient and budget-balanced. Trade price is calculated using simple k-double auction rule (Friedman, 1993). The algorithm generates more social welfare as compares to standard technique without grouping and improves the resource utilization. Convergence and optimality of the grouping mechanism is evaluated both theoretically (Establishment of Nash equilibrium) and experimentally (Simulated in all settings). However, the work mainly focuses on the allocation mechanism in double-sided trading of resources and a little attention has been given to payments schemes. In this work, total social welfare is distributed among all users and providers proportional to their valuations. For trading price determination first the total payment paid by each group is determined using k15

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double auction rule and after that the total payment is divided among group users proportional to their valuations.

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(Chichin et al., 2015) proposed a family of greedy based combinatorial double auction allocation mechanisms for cloud computing systems. They mainly focused on designing the allocation mechanisms and proposed two types of sorting criteria for homogeneous and heterogeneous resources in cloud settings. For homogeneous resources, they consider the Resource Relative Relation (RRR) function as a sorting criteria (Chichin et al., 2015). RRR function considers the surplus generated from trading of resources. According to this sorting criterion, a trade is more efficient if it generates more surpluses for fewer numbers of traded resources. Resource Scarcity Factor (RSF) is used for the sorting of bids for heterogeneous types of resources which is also suitable for dynamic resource provisioning. According to this criterion a trade is more efficient if it generates more surpluses for fewer numbers of traded scarce resources. All the sorting criteria are evaluated in terms of allocative efficiency, social welfare and resource utilization. The greedy mechanisms proposed in the work increases resource utilization and social welfare. But the work focuses only on the allocation mechanisms and no pricing scheme was proposed.

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A Credibility-based resource allocation model was proposed for Cloud media resources in (Tang et al., 2014) where credibility demonstrates the trustworthy rate between the different media resources of cloud system. Here, credibility between the resources is considered. Resource allocation among the resource owners and resource applicants is performed using the allocation agents. First, cloud users or resource applicants and resource owners submit their bids. Bids of resource applicants contain some information such as task deadline, number of required resources, highest price it is willing to pay etc. whereas resource owner bid contains the information e.g. number of remaining resources, minimum acceptable price etc. Allocation agent allocates resources based on final trade prices which are calculated by taking average of maximum bid price of resource applicant and lowest price of resource owner. After adjusting the bid prices of all the participants, final trade prices were calculated by sorting the prices using quick sort. An optimal allocation sequence is obtained by setting the maximum utility function. After that, cloud media resources are allocated using the credibility of the optimal allocation sequence. The algorithm is compared with FIFO and CDA (Dawei Sun et al., 2010) in terms of successful execution rate and response time.

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(Sun et al., 2015) proposed a group buying based VM allocation mechanisms for cloud computing. The work incorporated the group buying behavior of users in combinatorial double auction mechanisms for availing the discounted resource prices. Two mechanisms, based on greedy allocation schemes and critical proportional value pricing, were proposed and compared with (Samimi et al., 2015) in terms of the number of winning users, total social welfare and resource utilization. The work considered only one side combinatorial bidding and the truthfulness is proved only for the cloud users. (Sabzevari and Nejad, 2015) addressed the resource allocation problem using combinatorial double auction by maximizing the total social welfare. In Combinatorial double auction, ICA algorithm is used for winner determination, while using Genetic algorithm for resource allocation. In Pricing phase, K-Pricing scheme is used which is budget-balance but not truthful. The study conducted in this section reveals that designing truthful double auction mechanisms for simple environment are easy and sound, analytically and practically. But when more complex environment or markets are considered like discriminatory pricing, multi-dimensional and 16

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combinatorial bids, multiple resource configurations, then to design truthful double auction mechanisms become very complex. In most of the cases, truthfulness is preserved for one side only i.e. buyer or seller side.

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The literature survey and the comparative study of DA mechanisms in cloud computing reveal that double auction is used for different problems in cloud computing. For example, (Chichin et al., 2015) (Lee et al., 2015) (Izakian et al., 2010) (Wang et al., 2015) etc. mainly used the double auction for resource allocation problem whereas some of the works (Samimi et al., 2015), (Baranwal and Vidyarthi, 2015) (L. Wang et al., 2014) etc. used the double auction for resource pricing. Some works have been done for the resource allocation and pricing both with the help of double auction mechanisms. Most of the works do not consider the complexity and nature of cloud market (heterogeneity of resources, perishable resource, QoS provisioning etc.). Also, some of the works have design the DA from a mechanism design perspective describing all MD properties theoretically as well as practically, but they considered simple environments which are not feasible and suitable for complex market such as cloud computing market.

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The comparative study of double auction mechanisms for cloud computing is shown in Table 2. Table 2: A Comparison of Double Auction Mechanisms in Cloud Computing

Double Auction in Cloud

Auction Properties

Research Objectives

IC

IR

BB

(Shang et al., 2010)









(Samimi et al., 2015)









(Fujiwara et al., 2010)









(LI et al., 2009)





Experi mental enviro nment

Benc hma rk

SUSI,MUSI, MUMI,COM B

Design optimal pricing strategy based on Double auction and Bayesian Game, Propose uniform, flexible and competitive framework for cloud market Combinatorial Double Auction with greedy allocation and Average Pricing Mechanism Propose CloudAuction – A java based simulator for Auction models in cloud computing

Lacks performance Evaluation and analysis

Empiri cal Study

--

SUSI/SUSI

Not Incentive Compatible, Experimental studies not exhaustive

Rand omly Gene rated Data

COMB/MUMI

An economically Efficient Marketplace for cloud computing Two markets – Forward and Spot markets Service Allocation -- MIP Combinatorial Double Auction based resource allocation and pricing in Grid computing Resource Allocation Scheme - Greedy method Pricing – Average method

Not Incentive Compatible Inefficient when cloud market size increases Lacks detailed analysis

Cloud Auctio n – java based simulat or WMart Simula tor

Rand om Data

---

Not Incentive Compatible, Not individual rational

Simula tion

Rand om Data

COMB/MUMI

Grid Simula tor using JADE Simjav a 2.0 toolkit

Rand om Data

SUSI/SUSI

Rand om Data

COMB/MUMI

Rando m Data

Clou dSim

SUSI/SUSI

PT

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EE

Drawbacks

CE











-

Resource Allocation in Grid using Continuous Double auction (CDA) Intelligent Bid Price Calculation, Average Price mechanism

Focus on resource allocation strategy, Not emphasis on pricing Not Incentive Compatible

(Sun et al., 2013)



-

-

-

Not efficient, Auction properties not discussed Mechanism design perspective has not been taken

(Tang et al., 2014)

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Intelligent agent based Combinatorial Double Auction WDP – Global Search Optimization Method Feedback Rating based Reputation scheme Back-Propagation based Neural Network for Bidding Price Decision, Uses historical bidding Statistics Credibility based Cloud Media Resource Allocation (CCMRA) model Improved resource utilization and

AC

(Izakian et al., 2010)

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Auction discussed

properties

not

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(Wang et al., 2015)



(X. Wang et al., 2014)

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-

-

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performance QoS. Intelligent Economic approach – Improved Combinatorial Double Auction WDP – Paddy Field Algorithm (PFA) Reputation scheme Back-Propagation based Neural Network for Bidding Price Decision DMAA – Double Multi-Attribute Auction Allocation – MVO Pricing – SVM QoS Management – NNA Multi – unit Double Auction Based Inter-cloud VM trading mechanism

Not efficient, Auction properties discussed.

Simjav a 2.0 toolkit

Rand om data

COMB/MUMI

Not economic efficient

Cloud Sim

Rand om Data

MUSI/MUSI

Lacks Experimental studies and analysis

Simula tion

Rand om Data Rand om Data

MUSI/MUSI

not









(Zheng et al., 2014)









Two truthful Double Auction based for multi cloud multi-tenant bandwidth reservation

Not studied for allocation in cloud

resource

Simula tion

(Lee et al., 2015)









Proposed mechanism is not incentive compatible

Simula tion

Rand om Data

COMB/MUMI

(Chichin et al., 2015)









No pricing addressed

Mechanism

Simula tion

--

COMB/MUMI

(Sun et al., 2015)







Not IC for providers. One side truthfulness is proved

Simula tion

Rand om Data

COMB/MUMI

(Wu et al., 2016)







Simula tion

Rand om Data

SUSI/SUSI







Auction properties not discussed. Does not consider the complexity of cloud market

Cloud Sim

Rand om Data

MUSI/MUSI

(Baranwal and Vidyarthi, 2015)









Two novel economic mechanism for scalable and automatic resource 1. Modified Vickrey Mechanism (MVA) 2. Continuous Double Auction (CDA) mechanism Double auction based trading prices calculation and Cell Membrane Optimization (CMO) based Resource Allocation. Optimization values are introduced A fair multi-attribute combinatorial double auction, Solves bidder drop problem, Robust model

Not IC for cloud users

(L. Wang et al., 2014)

(C lod Con sum er only ) (P rovi der only ) 

A real Time Group Auction based Cloud Instance Allocation Allocation – Grouping Based K-Double auction pricing Rule Greedy based Combinatorial Double Auction, No pricing Mechanism Proposed Group Buying of users in Combinatorial Double Auction Greedy based Allocation Pricing Critical Proportional value pricing

Not incentive compatible, not individual rational

Rand om Data

COMB, MUMI

(Sabzevari and Nejad, 2015) (Prodan et al., 2011)











Not Incentive Compatible, Pricing Schemes not discussed. Not Incentive Compatible

Rand om Data Rand om Data

COMB/COMB



Combinatorial double auction for cloud computing using ICA and Genetic Algorithm Continuous Double Auction for scheduling scientific application on heterogeneous computing resources in grid and cloud environments.

Cloud Auctio n Simula tor Cloud Sim

An efficient and online combinatorial double auction mechanism for resource allocation in mobile cloud computing (MCC) market. market-driven continuous double auction for cloud computing

Not Incentive Compatible

Rand om Data

COMB/COMB

Rand om Data

SUMI/MUSI

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AC

(Farajian and Zamanifar, 2013)

CE

(Xu et al., 2014)



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(Li et al., 2013)

Not incentive Compatible, focus on scheduling strategy rather than market design

GridSi m with Auctio n Frame work Simula tion

JADE,

MUMI/MUMI

SUMI/MUSI

Economic Efficiency (EE), Individual Rational (IR), Incentive Compatibility (IC), Budget-Balance (BB), SingleUnit Single-Item (SUSI), Multi-Unit Single-Item (MUSI), Multi-Unit Multi-Item (MUMI), Combinatorial (COMB)

3. A Framework for Double Auction in Cloud Market With the increasing popularity of cloud computing, the size of cloud market has also increased at a fast pace. At present, a number of cloud providers offer cloud services to the cloud users with differential pricing and varying QoS attributes. With time, more and more startups and big 18

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corporates are expected to adopt the cloud platform. In line, there are large number of cloud users who want to execute their tasks or jobs using cloud resources with minimum prices and better QoS. Cloud providers are interested to maximize their profit or revenue by selling their resources at high price while meeting the SLAs. To achieve their respective objectives, both the cloud providers and the users compete strategically in the cloud market. A way to handle this both-side competition is design of a double auction based mechanisms. In this work, a framework for futuristic scenario of double auction based cloud market is proposed which can be used by professionals and researchers as a basic framework for designing double auction mechanisms.

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Consider a cloud market that consists of multiple cloud users and cloud resource providers involved in buying and selling the cloud resources. In double auction, both the cloud users and providers submit their bids to the cloud auctioneer. Cloud user’s bid is comprised of the information regarding the quantity of the required resources and the valuation (price) for these resources. Similarly, the Cloud providers submit their bids comprising of the quoted resource quantity and their price. The details of the double auction market are presented below. Cloud User

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A cloud user is a customer interested to avail the cloud services. The services may correspond to some resources e.g. Virtual Machines (VMs) to run its applications or to host some of its services. All the cloud users host different applications or services and hence require different QoS from the cloud provider. Assuming that the cloud resources or services are limited in nature, these users compete for the cloud services aiming to procure it at minimum cost with desired QoS.

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Depending on the requirement, a user can generate a bid for the set of VMs comprising of various resources offered by the cloud provider. Depending on the requirements, a bid value is generated reflecting the user’s actual valuation of the resource according to its budget. This bid value is private information to the cloud user. Users are assumed to be greedy aiming to maximize their utilities and can manipulate the system by bidding untruthfully. Meaning thereby that the user can bid much lower or higher than their actual valuations which not only harm other users but also translates in a loss to the provider. Therefore, the revenue of a cloud provider can’t be maximized until strategy proofness is enforced into the auction mechanism. A way to do so is to implement the dominant strategy incentive compatible mechanism in which being truthful is the dominant strategy for each user i.e. the bidder’s utility will be maximum if the bidder bids truthfully. Cloud Provider A cloud provider will act as a seller and is an organization or business entity that provides the cloud computing services like computing, storage, network etc. to the cloud users. The objective of a cloud provider is to maximize their profit while offering the cloud services at highest price with minimum acceptable QoS. As there are multiple cloud providers in the cloud market and each wishes to maximize its profit, the market turns into a competitive market. The competitive market brings dynamic pricing of cloud services to bring more revenue to the cloud provider as compared to the static pricing mechanism. 19

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Cloud Auctioneer Cloud Auctioneer is an entity (a broker, organization or a company) which performs the auction acting as an interface between the users and the providers. The auctioneer keeps all the information about the cloud market and its various parameters such as cloud resource configurations, VM types, number of participants etc.

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The cloud users and providers submit their respective bids to the auctioneer. After receiving all the bids, the auctioneer closes the auction to find the winning set of users and providers with the proposed allocation mechanism. Here, the winning set of users comprises of those users who successfully get their requested bundle of resources. A winning provider is successfully chosen to sell its resources to the users. The auction results are communicated to both the user and the provider and final trade price is derived for the whole resource allocation. The given framework of double auction mechanism for cloud market defines the organization of the cloud system model, information exchange process between the users and the providers, resource allocation procedures and clearance rules (trading prices or pricing mechanism) of the market. The complete double auction model is represented algorithmically and by the sequential diagram in figure 1.

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0. Begin 1. Cloud Auctioneer starts auction. 2. Cloud Users and Providers submits bid information (private type) to Cloud Auctioneer. 3. Cloud Auctioneer acknowledges the Cloud Users and Providers for their bids. 4. Cloud Auctioneer closes the auction. 5. Cloud Auctioneer informs the Cloud Users and Providers about closing of the auction. 6. Cloud Auctioneer screens the bids of Cloud Users and Providers and solves allocation phase i.e. winner determination problem. 7. Cloud Auctioneer sends the result of the winning determination problem to the winning Users and Providers. 8. Winning Cloud Providers allocate the resources to winning cloud Users based on the allocation result. 9. Cloud Auctioneer calculates the final trade price between the Cloud Users and the Providers Based on the Allocated Resources. 10. Cloud Auctioneer informs the trade prices to the Cloud Users and the Providers. 11. Cloud Users pays the Cloud Providers calculated according to the final trade prices. 12. End

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Figure 1: Sequence diagram of TMDA

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4. Truthful Multi-Unit Double Auction (TMDA) in Cloud Computing

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In previous section, numerous double auction mechanisms have been presented in cloud computing. Most of these mechanisms focus on the allocation function which matches the most eligible cloud users and providers for resource trading. A few of these works focus on the design of truthful payment schemes but the truthfulness is considered only for the user. As, both side bidding is involved, the auction mechanism should ensure to be truthful for both the sides in a sustainable cloud market. As per the author’s best knowledge, no double-sided truthful double auction model has been reported in the literature for resource allocation in cloud computing. This section presents the proposed double-sided truthful multi-unit double auction (TMDA) mechanism for the cloud resource allocation problem. TMDA also helps understand researchers on how to design a double auction mechanism with desired auction properties in a chosen cloud scenario. 4.1 Formulation of TMDA A cloud market is just like any other competitive market where some market rules and mechanism are applied along with some technical details such as VM resource configurations and other cloud system details. Cloud users execute certain jobs or tasks on VMs provided by the cloud providers. A cloud provider generally provides the resources in form of Virtual Machines (VMs) which consist of different resources e.g. CPU, memory, storage, network etc. Here, VMs 21

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are traded in the proposed auction market. These VMs are provided to the cloud users with varying QoS attributes such as service response time, reliability, throughput, efficiency etc. Before the TMDA formulation, notation used in the model are presented in Table 3. Basic Formulation

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Resources are provided to the cloud users in form of VMs. A real world example of this is the Amazon EC2 instances (Amazon, 2011). Amazon offers various types of VMs with different resource configurations with differential pricing schemes. Initially, cloud user can bid for a single VM at one time and in case requiring more VMs, it has to bid again for another spot instance. Recently, Amazon has started to offer multiple VMs of the same type. The required VMs are allocated to the winning users by picking the VMs from Spot pools and spot fleets (“Amazon EC2 Spot Instances”). This work considers a cloud environment with homogeneous VMs and users with multi-unit demand and providers’ multi-unit offers. The double auction mechanism proposed and explained in the coming sections has the features of being flexible, individual rational, budget-balance, truthful for both cloud users and providers and efficiently allocates the VMs among the cloud users. Table 3: Notation Description Total number of cloud users Bid vector of User Number of VMs required by Bid price (per - unit) of Bid vector of

Notation

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Number of VMs offered by provider Bid price (per - unit) of provider Per-unit payment by user Quantity offered by provider Quantity received by user Per-unit payment obtained provider

Description Total number of cloud providers Utility of user Utility of provider Final trade price between provider Set of cloud users

user and

Set of cloud providers

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Number of winning users Number of winning providers Social welfare generated in cloud market Total profit of the auctioneer

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Bid Information of Cloud Users

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Let us assume that there are cloud users in the cloud market who need cloud resources for the execution of their jobs or tasks. Depending upon its requirement, a user estimates the number of resources required and formulates its total demand by stating the number of VMs and its estimated bid price. A user , , bids for the resources in form of a number of VMs for its job’s execution where represents number of VMs requested by a user . In addition, the user specifies a bid value , per-unit price for requested resources i.e. the maximum price it is willing to pay per unit if it gets the quoted resources. Its bid can be specified as a 2-tuple bid vector as given in equation (1). (1) Bid Information of Cloud Provider 22

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Let us assume that there are cloud resource providers in the cloud market. These providers participate in the auction for selling their VM resources to the cloud users. A cloud provider offers cloud services to the users at different prices with correspondingly varying QoS attributes. For simplicity, it is assumed that a provider offers the services at considerable QoS level (or required by user). A provider’s bid can be represented as a vector comprising quantity of resources on offer and the corresponding per-unit ask price for those resources called the ask price. The ask price of a provider represents the minimum price at which the provider can provide its services and depends upon various parameter such as running cost of the VMs, users’ utility for those resources, ask prices of other providers etc. The bid from the provider can be specified as a 2-tuple bid vector as given in equation (2). (2)

Where represents the number of VMs for which the provider is bidding with bid price.

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Assumptions

being the

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While designing the model, it has been assumed that the valuation of the resources by a cloud user is a confidential information that can be misrepresented during bidding. On the other hand, the quantity of the required resource can be treated as public information which cannot be misrepresented. Usually, in an e-market and especially in the cloud, the cloud users reveal true information regarding their required quantity of resources. However, in case of providers, the valuation of the resources available for sale or allocation as per its bidding price is private and can be misrepresented for its own benefits. In addition, a cloud provider can also be dishonest about the quoted quantity of resources for allocation to increase its own utility. In some cases, a cloud provider may get some benefits by underreporting its valuation while in some other cases its utility will decrease if it is not revealing the private information truthfully. In this work, the strategy of underreporting of valuation by a cloud provider is not implemented due to the following reasons.

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1. The strategy of underreporting can benefit only the winning providers but no provider can be certain about winning the auction. 2. Even when a cloud provider assumes that underreporting the valuation could benefit but how much underreporting can be reported remains a question as underreporting beyond a certain limit can also decrease the utility (in some cases) (Vickrey, 1961). 3. Consider the scenario in which all cloud providers have all the information about the quantity each provider wants to offer. This could drive all the providers to a point deciding on how much they can underreport their valuation. In that case, each provider would want to underreport its valuation (being untruthful) causing the worst market manipulations. This situation leads to a problem referred to as “common knowledge” in the game theory. Details of the Base Model The proposed TMDA model consists of several phases detailed as follows. Start of Auction

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The auctioneer starts the auction and invites customers and providers both for the bidding. Submission of Bids

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All the users and the providers report to the auctioneer with their corresponding bid values. Each user , generates its requests for VM resources as per the application or job requirements. After generation of VM requests, the user participates in the auction by bidding for these resources coupled with the bid price. Similarly, each provider bids for its resources i.e. each provider advertises its resources with an offered price. Winner determination and Payment Methods

Given the bids information from the users and the providers, the proposed allocation algorithm determines the winners in the auction system in such a way that the social welfare (sum of utilities of the participants) gets maximized.

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After receiving the resource requests from both sides, the auctioneer calculates the quantity traded between cloud user and cloud provider . Assuming to be the final trade price for cloud user and provider , the utility of the cloud user can be represented as the difference between its valuation and the price actually paid to the cloud providers for using their cloud services as given in equation (3). ∑

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Similarly, the utility of a provider can be calculated as the difference between the total payment it gets from cloud users and its bid price as given in equation (4). ∑

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(4)



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(

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(5)

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Given the utility function of user and the provider, the winner determination problem is formulated to maximize the social welfare i.e. sum of utilities of all the participants as given in equation (5).

(6) (7) (8)

The above two constraints states that a buyer’s allocated capacity should not exceed its demand and a seller’s traded resource capacity should not exceed its available capacity. It is to be noted that the total social welfare does not depend upon the final traded price but the payment done between the user and provider do affect their utilities. In constraints (6) and (7), and is the set of cloud users and cloud providers respectively.

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The above problem is a LP formulation of the maximization problem with many constraints and has many solutions. The above problem is solved keeping in mind the property of strategy-proof and to maximize the utility of both the agents. First all the cloud users are sorted according to their quoted prices in a decreasing order as given in equation (9). (9)

(10)

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Similarly, the providers are sorted in increasing order of ask prices as given in equation (10).

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After sorting the users and the providers, the demand quantities are matched with the supplied resources one by one. After some successful trades, there will a point where demand and supply will match. Suppose and are the number of cloud users and providers respectively who can successfully buy and sell the resources, then there are the two conditions according to supplydemand principle as given in equations (11) and (12). (11)

or

(12)





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∑ or





(13)

(14)

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The number of resources, traded in these two cases, will follow respectively as given in equations (13) and (14).



(15)



(16)

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For the first case, following the first scenario, there are further two possibilities. Either the total required resources are more than the available ones in total traded quantity of resources indicating an over demand of resources as given in equation (15) or the number of resources provided by the cloud providers are more than the resources required by the cloud users indicating an oversupply of the resources as given in equation (16).



In case of over demand of resources, the overall traded quantity that is over demanded will be distributed among all the cloud users i.e. the quantity allocated to the user will be given in equation (17). (∑



)

⁄∑

(17)

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The above method of partially allocating the resources to the user raises various concerns e.g. even after winning the auction, why a participant has partial allocation. The applicability of the method can be strengthened by the fact that in auction a participant is not sure about winning. Even in Amazon spot market (“Amazon EC2 Spot Instances”), a user is not sure about getting all the resources after winning.

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The trade price calculation will follow the procedure of dominant strategy double auction (McAfee, 1992) that is based on the Trade Reduction (TR) approach. For the sake of truthfulness, the efficiency of the mechanism needs to be compromised resulting in the removal of least profitable trades. Therefore, all the cloud users with index pay the prices equal to and all the providers with index get the payment equal to . In case of oversupply, the total quantity of resources that is oversupplied are distributed among all the providers. Hence, the total quantity provided by the provider will be as given in equation (18). ∑

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⁄∑

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(∑

In the above case, if there is any oversupply, then a provider is always providing equal or less than its quoted quantities. This way of allocating the resources gives a room to the providers for manipulation of the market by representing false information about the quantities. In this case too, all cloud users with index will pay the prices equal to and all provider with index will get the payment equal to .

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From the above equations, it can be shown that total traded resources will be the minimum of the ∑ total required quantity and the total available resources i.e. (∑ )

(∑

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Total Trade Surplus =

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The total trade surplus generated from the double auction mechanism depends upon the total traded quantity and the difference between the non-traded bid and ask price. Total surplus can be calculated as given in equation (19). ∑

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The algorithm for the proposed TMDA mechanism is presented below.

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Algorithm: TMDA

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Input: Output: Initialization Cloud users and Cloud providers submit bids to the auctioneer. The auctioneer collects the information and initializes the system parameters. Bidding Parameters User bid i CU Provider Bid j CP Winner Determination and Payment Schemes  Sort users such that  Sort providers such that  Find R and S such that condition 1 or 2 holds where Condition 1:

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&& ∑ and Condition 2:



&& ∑











// Overdemand (∑ ∑

// Oversupply (∑

∑ Total trade Quantity = ∑ Profit = (∑

4.2 Auction Properties of TMDA



∑ )

⁄∑

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The auction properties of TMDA is as follows.

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Efficiency

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The proposed mechanism is asymptotically efficient. The mechanism achieves 100% efficiency when the number of auction participants is large. The theoretical proof of 100% efficiency in multi-unit double auction mechanisms is given in (Huang et al., 2002). The mechanism picks up the most eligible users and providers among all the participants depending upon their bid prices and sacrifices the least profitable trade for ensuring the truthfulness. Budget-Balance

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The proposed mechanism is weakly budget-balanced i.e. a non-negative surplus i.e. ∑ (∑ ) is generated from the proposed double auction mechanism as the difference between and and will always be equal or greater than zero. This surplus is paid to the market maker or auctioneers i.e. the entity which holds the auction. It can be treated as the charge for holding the auction or establishment of cloud market. Incentive compatibility As discussed earlier, most of the double auction mechanisms proposed in the literature are truthful for only one side i.e. the buyer’s side. This results in the mechanism forcing the buyers to bid truthfully in the auction whereas the provider can wrongly manipulate the market. This 27

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demands that the double auction mechanism should be truthful for all the participants for both the sides. This section proves the truthfulness of TMDA for both the cloud user and provider. The proof of truthfulness for all the participants can be inferred from the Vickrey arguments (Vickrey, 1961).

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For cloud user: In the proposed mechanism, all cloud users who successfully trade the resources pay the amount equal to the valuation of the last user in the list i.e. which is the non-traded user in the final allocation. The truthfulness ensures that a participant’s utility will be maximum if it reveals its information truthfully. This can be proved by considering all possible cases and then showing that its utility will be maximum only when it bids truthfully.

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a. Cloud user wins: 1. If bid value < true valuation, then there are two cases  It still wins. Therefore, there is no change in its utility.  If its bid value is less than the critical payment i.e. which is the bid value of the losing cloud user, then it shifts to the position in the list and loses. Its utility in this case will be zero. 2. If bid value > true valuation, then it will be always a winner because its position in list will go upward (monotonic allocation function). b. Cloud user loses: 1. If bid value < true valuation, then it will still be a loser in the auction because lowering the valuation further puts it in the lower position in the list (monotonic decreasing function). 2. If bid value > true valuation, then there are two cases  It may still be the loser when it does not qualify the winning bid’s amount.  It may be the winner but in that case it will pay more than its valuation. Therefore, in this case, its utility will be negative and it suffers loss.

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The truthfulness for the cloud provider can also be proved in the same manner as in the buyer’s case.

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Individual Rationality

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In the proposed mechanism, all winning cloud users always pay less than their valuation and all cloud providers always get more than theirs ask price. The trading prices will be zero if the participant does not win in the auction. Therefore, every participant gets either positive or zero utility deeming the proposed mechanism individually rational. 4.3 An Illustrative Example of TMDA This section presents an illustrative example to help understand the proposed combinatorial double auction mechanism TMDA. Let us consider the bid matrices and other information of the users and providers as given in Table 4(a) and Table 4(b) respectively. A simple bid values of both users and providers have been considered to easily understand the mechanism. A user’ bid vector consists of the bid price 28

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and the quantity of resources while the provider’s bid vector contains the offered resources and the ask price for those resources. Let N be the number of cloud users and M be the number of cloud service providers. For the example, let . Table 4(a) Bid Price 47 45 35 34 33 22 12

Quantity 8 2 3 1 8 7 5

Final Traded Quantity 8 2 3 1 0 0 0

Utility 14 12 2 1 0 0 0

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User id

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For simplicity, it is assumed that the users are sorted in the decreasing order of their bid price i.e. the user who bids highest gets the first position in the list and the lowest price bidder gets the lowest position in the list. In the same way, the providers are sorted in the increasing order of their bid prices. The double auction mechanism, proposed in this work, contains two main functions i.e. allocation function and the payment method. Allocation function is used to find the most eligible users and providers based on their bid prices and the resource quantity requested or offered. The model arranges the requested resource quantities in descending bid prices for the cloud users and as per the ascending ask prices of the cloud provider for the offered resource volume as per equations (9) and (10). According to the proposed mechanism, 5 users and 6 providers qualify in the list of successful users and providers. Among them, 4 users and 5 providers further qualify to trade the resources. The last trade is sacrificed for preserving the truthfulness in the proposed auction mechanisms.

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Provider id

Bid Price 15 17 20 22 25 30 49

Table 4(b)

Quantity

Final Traded Quantity 4 4 1 1 4 0 0

5 5 1 2 4 8 3

Utility 15 13 10 8 5 0 0

All users avail the requested resources at a per-unit price of 33, the bid price of whereas all the providers provides their resources at a price of 30, the bid price of . The quantity of the traded resources depends on certain situations as explained in section 4. In this example, the condition of oversupply occurs and demand and supply constraint is satisfied by adjusting the offered resource quantities of all providers and the adjustment (reduction in offered quantities) is directly proportional to their offered resource quantities. In this case, this exceeded quantity is 3 29

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which is divided among all five providers based on their offered quantities. After calculation, the traded quantities of the providers are shown in table 4(b). The total traded quantity of the resources thus is 14 and the profit generated by the auctioneer becomes . The utility of all the users and providers has been presented in the respective tables. 4.4 Simulation Study

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Simulation is better alternative for performance evaluation of TMDA, as creation of real cloud environment for the evaluation of TMDA under different configuration and requirements of system in a repeatable and controllable manner is not only costly but also time consuming and tedious (Calheiros et al., 2011). The proposed TMDA mechanism has been simulated using Matlab. Each experiment is run for 100 iterations and averaged to generate more precise results. The bid prices of the users and the providers are generated randomly between 1 and 100 and follow uniform distribution. Bid quantities of resources of all the participants are also generated randomly between 1 and 1000 with the same distribution. This depicts the dynamic behavior of TMDA.

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For comparative study, TMDA-First is considered. In TMDA-First, allocation is same as TMDA. The difference between TMDA and TMDA-First is in its pricing strategy. In TMDA-First, First Pricing is used. In First Price Auction, if a user wins, it pays its quoted bid value to the auctioneer. Similarly, if a provider wins, it gets paid its quoted ask price. First price auctions are not truthful and participants can manipulate the market (Vickrey, 1961). TMDA is compared with TMDA-First on various parameters such as users’ total payment, providers’ revenue. Along this, performance evaluation of TMDA is done using various performance metrics such as total social welfare, users’ utility, providers’ utility, total traded quantity. Case I: Fixed Users with Varying Number of Providers

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Double auction handles both side competitions in a market by allowing both side bidding. Users compete with each other to procure the resources while providers compete with each other for selling their resources. This section analyzes the effect of user’s payments when competition on the provider side increases i.e. the number of provider increases with a fixed number of users. The number of users is fixed to 10 and the number of providers varies from 10 to 100 with a step-size of 10. In this case, in TMDA, more providers with the small ask prices will be in the winners list hence resulting in less payment of the user and subsequently increasing the utility as shown in Figure 2(a) and 2(b). However, in the case of TMDA-First, users’ payment will be equal to their actual valuations which is always greater than truthful payments calculated in TMDA. This results in higher payments for the users in TMDA-First.

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Fig 2(a): Users’ Total Payment with Increasing Number of Cloud Providers.

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Fig 2(b): Users’ Total Utility with Increasing Number of Cloud Providers.

Case II: Fixed Providers with Varying Number of Users

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This case corresponds to the effect of the user side competition on the payment of providers and the amount that the providers receive for their cloud resources. Here, the number of providers is fixed to 10 and the number of customers varies from 10 to 100 with a step-size of 10. In this case also, when the number of user increases, more number of users with higher bid price will be in the winners list and eventually trade in the final allocation. This results in the higher payments for the providers. Therefore, more the number of users, more profit will be achieved by a provider, resulting in higher utility. Providers’ revenue and the utility of the providers for different number of users in this case of study is shown in Figure 3(a) and Figure 3(b). Figure 3(a) shows that TMDA generates higher payments for providers as compared to TMDA-First. The reason for the above result is that TMDA incorporates truthful payments which is always 31

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higher than its ask prices where as in TMDA-First, providers gets paid amount equal to their quoted ask prices.

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Fig 3(a): Providers’ Revenue with Increasing Number of Cloud Users.

Fig 3(b): Providers’ Utility with Increasing Number of Cloud Users.

Case III: Varying Number of Cloud Users and Providers Figure 4(a) presents the effect of different market size on the total utility and the total quantity traded. For individual experiments in this case, the number of users and providers are kept same. As the competition in the market increases i.e. more number of users or providers participate in the auction, more social welfare (sum of utilities of all agents) would be achieved by the double auction mechanism. The total quantity of resources that is traded in the final allocation increases 32

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with the increment in the number of participants. Figure 4(b) and Figure 4(c) shows the users’ payment and providers’ revenue with varying the number of participants. TMDA-First method generates higher payment for cloud users and lower revenue for providers as compared to TMDA model. The reason is same as explained in the previous section.

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Fig 4(a): Total Utility and Traded Quantity with Increasing Market Size.

Fig 4(b): Users’ Payment with Increasing Market Size

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Fig. 4(c). Providers’ Revenue with Increasing Market Size

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The above discussion demonstrates the behavior of TMDA mechanism for all possible dynamics in the cloud market such an increment in the number of cloud users or providers while comparing TMDA with TMDA-First mechanism. The social welfare and the payments of participants’ change is observed when the number of participants is increased. It is observed that the user payment decreases when the number of providers increases. This results in higher utility achieved by the users. TMDA generates lower payments for winning users as compared to TMDA-First due to truthful behavior of the users. Similarly, a provider gets more payment for its offered resources if there are more number of users in the market. TMDA generates higher revenue for winning users as compared to TMDA-First. It is observed that TMDA performs well in all the cases for utility and social welfare. The results show that TMDA maximizes the social welfare by trading the resources between most eligible participants and satisfies the incentive compatibility.

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5. Some Issues and Future Directions

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Some issues that arises from the study have been pointed out here along with some future research directions. 5.1 QoS Management in Double Auction based Models

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Quality of Service (QoS) is an important parameter in the context of cloud computing. The same is true for even the auction based models where a proper monitoring of QoS is required. In double auction based models, it is important that the auctioneer is a reliable agent (a person, business or company) having information about the cloud market. A double auction mechanism is useful for cloud market if the allocation and payment results are honored by all the participants i.e. both the user as well as the provider. It is possible that the QoS of the cloud services provided by a cloud provider is not up to the level as quoted while bidding. In that case, the auctioneer can initiate a legal action against that provider. Also, some other methods can be enforced into the mechanism (reputation, priority, penalty etc.) which can potentially harm the provider in any direct or indirect way. These issues have gained attention of the researchers (Baranwal and 34

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Vidyarthi, 2015), (Wang et al., 2012). In (Baranwal and Vidyarthi, 2015), both penalty and reputation diminishing is used for those cloud providers who don’t provide the services at quoted QoS. These methods fit well in designing recurrent, fair and egalitarian based double auction mechanisms. However, designing the truthful mechanisms considering all the above scenario is very complex and needs further research. 5.2 Truthful Revelation

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One of the main objectives of the double auction mechanism design is to achieve truthfulness. This property ensures that all the bidders are bidding their true valuation for their requested resources. This property seems quite important in the economic market design. If this property does not hold, participants can manipulate the auction market by reporting a bid value different than its actual valuation. This misreporting of bidding values affects the market in several ways. First, if all the bidders bid their false valuation then it may cause an unfeasible trade between all the bidders who also eventually affects all the participants. Second, it is impossible to get to know whether the auction design is efficient or not because no one has complete information of all the agents or the market. Also, in this scenario, a dishonest bidder can win by biding untruthfully leading to rejection of the right candidate and hence discouraging the bidders to participate in the auction. This can ruin the whole cloud auction market. Therefore, truthfulness is highly desirable in double auction. Normally, trading of cloud resources happens over the internet where the cloud providers and users are located at remote places. That way, it is not possible to force a bidder to bid truthfully (Parkes et al., 2001). However, the auction mechanism designer can design a mechanism that induces the bidders to bid truthfully making it in the best interest of each bidder, regardless of other bidders’ strategies. 5.3 Interoperability between Services of different Providers

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As discussed, a real cloud market based on the double auction has not been implemented yet. The issue of interoperability among the cloud providers thus becomes the main hurdle while designing the cloud market. This issue arises in the situation when the resources are allocated to a single user from more than one provider. The task of integration and the use of cloud services from more than one provider is very complex. Also, switching cost comes into effect when a cloud user decides to change the service provider for some reason (Beall et al., 2003).

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The proposed TMDA model considers the multi-unit single item scenario as the base model. As already discussed, it is assumed that a cloud provider has a rack of servers in their data centers and homogeneous types of VMs can be provisioned depending upon the demand of the cloud users. All the providers do not provide resources with same configuration. They have their own unit to measure the resources. Recently some business professionals have provided common unit to measure virtual computing power offered by various providers (CloudHarmony, 2010). For example, Amazon (Amazon, 2011) defined “EC2 Compute Units” or ECUs as a measure of virtual computing power which has been defined as a 40 ECU where one ECU is a standard unit (e.g. VM in this case) for computing. Similarly, standard storage units can be described in the same manner. Same as market maker or the auctioneer may provide some common standard for measuring various types of virtual computing power by various providers. 5.4 Designing Multi-Attribute Double Auction Mechanisms 35

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Most of the works, related to double auction in cloud computing, consider the uni-attribute scenario taking price as a parameter. QoS is equally important parameter in the cloud computing. (Abdelmaboud et al., 2015) discussed the recent work on QoS approaches in cloud computing. As cloud based market considers various QoS parameters in the SLA agreement, multi-attribute auction fits well in this type of environment. Searching for the most eligible candidates in such cases is very complex as it will depend on many factors. In addition, some of these attributes are quantitative type while some qualitative. Quantitative attributes are those attributes which can be measured by some measuring or monitoring tools and qualitative are those which can’t be measured or quantified and are evaluated mostly based on the user experience. Designing the strategy-proof mechanisms in these environments requires strategic payment schemes.

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In cloud computing, IaaS providers provide different types of VMs in cloud market. Therefore, in this context, quantitative attributes become more relevant. Some examples of such type of attributes for IaaS clouds are service response time, availability, reliability, throughput, efficiency etc. which may be considered while designing the multi-attribute double auction mechanisms. A list of important quantitative cloud QoS attributes with their definitions and examples have been given in (Baranwal and Vidyarthi, 2016). Some other works (Baranwal and Vidyarthi, 2015), (X. Wang et al., 2014) have even considered various non-price attributes along with price while solving the winner determination problem. 5.5 Computational Complexity of Combinatorial Double Auction

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Basic formulation of the traditional combinatorial double auction problem is formulated as an NP-Hard problem (Xia et al., 2005). This is true in the case when any combination of the goods is allowed in a bid. As the problem is NP-Hard, to establish the exact solution for this problem is computationally intractable for the large input size. Researchers have proposed various approaches to solve this problem using heuristics and approximation techniques to find the near optimal solutions in polynomial time. (Rothkopf et al., 1998) addresses the winner determination problem in combinatorial auction by relaxing some of the constraints. Authors, in the work, observe that rather than allowing all the combination in a bid, if a subset of all the combination is allowed, the problem gets reduced to LP and can be easily solved in polynomial time. But the constraints considered in (Rothkopf et al., 1998) does not apply in real combinatorial auction. Sometimes, there are some cases that the auctioneer can’t enforce the bidders to bid in some limited combination of goods. For example, it is unreasonable to enforce the cloud user or provider to bid for some specific types of VMs. The work proposed in (Sandholm, 2002), (Fujishima et al., 1999), (H.~Hoos and Boutilier, 2000), (Sandholm and Suri, 2003), (Sandholm, 2001), (Sandholm et al., 2001), (Hsieh and Liao, 2015), (Hsieh and Liao, 2013) addressed the winner determination problem in combinatorial auctions through various approaches. 5.6 Bundling

Combinatorial auction generates greater revenue and maximizes the market economic efficiency by allocating the resources to those who value them most (Milgrom, 2000). Bundling in combinatorial auction is also an important issue. Formation of bundle requires decision on the size of the bundle, types of VMs placed in the bundle, number of each type of VMs, non-price attributes of the bundle or price of the bundle to name a few (Reyes-Moro and Rodriguez36

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Aguilar, 2005). These decisions require understanding of the auction’s dynamic, accurate knowledge of experience and interest of customers in bundles (CATS, n.d.) (Vinyals et al., 2008). Market history can be really helpful in this condition. But generally market does not reveal information of bundle and its pricing made by all providers. It only reveals information of winning bundle and traded price. A bidder’s decision making would depend on its winning/losing bundle, its risk and its attitude towards market (Nassiri-Mofakham et al., 2015).

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5.7 Discriminatory Bidding and Non-Uniform Pricing

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Discriminating bidding happens when multiple units of same item are traded at different prices. For example, a buyer is likely to pay less for large quantity compared to payments made for less number of items. Another case may be that a provider wants to offer discounted prices to the customer who want more number of VMs. In the case of discriminating bidding, market clearing becomes a complex task and is an NP-Complete problem (Sandholm and Suri, 2001). In this work, uniform pricing of VMs has been considered i.e. all units of VMs have same price to accordingly design the payment schemes. In future, more complex settings and environments such as combinatorial and discriminatory bidding, non-uniform pricing of resources etc. can be considered.

6. Discussion and Conclusion

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Market-based Double Auction mechanisms are poised to play a greater role to implement the dynamic pricing and have recently been infused for selling the underutilized and spare cloud resources by even dominant cloud providers such as Amazon. In a cloud market where VMs are traded among its users and providers, the double auction framework becomes very appropriate helping to avail the auction benefits for cloud services. This work discusses the basic double auction mechanisms in detail along-with the auction properties and details a literature survey on how these mechanisms evolved from simple to complex environments. A comparative study of the double auction in cloud computing has also been done. The comparative study considers various parameters viz. merits and demerits of the proposed work, auction properties, study environment, benchmark, experimental method etc. Encouraged from the study, a framework for double auction for cloud market is given which helps researchers and business professionals to design double auction based markets for cloud computing. A truthful multi-unit double auction model (TMDA) is proposed for resource allocation and pricing in cloud computing which is truthful for both user and provider. Various issues and challenges, often a bottleneck for the realization of double auction based cloud market, are discussed in detail.

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The resource allocation method performing partial allocation (because of demand and supply principle) proposed in TMDA resembles the resource allocation method in cloud spot market. In amazon spot market (“Amazon EC2 Spot Instances”), a user is not sure about getting all the resources after winning because spot instances can be terminated at any time due to several reasons e.g. low bid price, out of capacity etc. Applications having flexible start and stop time are suitable for auctioning and applications without having flexible start and stop time needs some fault-tolerant mechanism such as check-pointing which is itself an overhead. If time period, for which they need resources, as specified by the customer in their bid formation is considered (Samimi et al., 2015) (Baranwal and Vidyarthi, 2015)[CDARA, FMCDAM], there would not be any interruption for that period if customer claims the resources (assuming there is 37

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no hardware failure). In this case, there would be no need to implement fault-tolerant mechanisms. All the applications such as workflow applications, multi-threaded applications etc. which may wait for some time to get resources can be implemented in this case.

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The proposed work provides a framework to help researchers and business professionals to design double auction based market for cloud computing and also help them to design double auction mechanism with desired auction properties for the trading of the computing resources. Though proposed TMDA model considers less complex environment i.e. multi-unit, but future work would consider more complex bidding structure such as combinatorial bidding, configurable offers, discriminatory pricing of resources, inclusion of QoS parameters etc. Future work is intended to be more focused on designing truthful (both side) double auction mechanisms for cloud computing consisting of combinatorial requests and offers. References

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Dinesh Kumar is PhD student in Computer Science at the School of Computer and Systems Sciences, Jawaharlal Nehru University, New Delhi, India. His research interests include resource provisioning and pricing in cloud computing.

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Gaurav Baranwal is a Faculty in Department of Computer Science and Engineering, MMMUT, Gorakhpur, UP, India. His research interests include resource provisioning and service coordination in cloud computing.

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Zahid Raza is a faculty in the School of Computer and Systems Sciences, Jawaharlal Nehru University, India. He is M.Sc. in Electronics, M. Tech in Computer Science. He did his Ph.D. in Computer Science from Jawaharlal Nehru University, India. Prior to joining JNU, he served as a Lecturer in Banasthali Vidyapith University, Rajasthan, India. His research interests include parallel and distributed systems, evolutionary algorithms and multi-objective evolutionary algorithms. He has published many peer-reviewed articles and has proposed various scheduling models for computational grid, cloud and cluster systems.

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Deo Prakash Vidyarthi is Professor in the School of Computer and Systems Sciences, Jawaharlal Nehru University, New Delhi. He was associated with the Department of Computer Science of Banaras Hindu University, Varanasi for more than 12 years before joining JNU as Associate Professor. Dr. Vidyarthi has published around 75 research papers in various peer reviewed International Journals and Transactions (including IEEE, Elsevier, Springer, Wiley, World Scientific etc.) and around 45 research papers in proceedings of various peerreviewed conferences in India and abroad. Dr. Vidyarthi has authored two books. One entitled “Technologies and Protocols for the Future Internet Design: Reinventing the Web” published by IGIGlobal (USA) released in Feb. 2012, and another entitled “Scheduling in Distributed Computing Systems: Design, Analysis and Models” published by Springer, USA released in 2009. He also has contributed chapters in many edited books. He is in the editorial board and in the reviewer’s panel of many International Journals. Dr. Vidyarthi is the member of the IEEE, Senior member of the International

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Association of Computer Science and Information Technology (IACSIT), Singapore, International Society of Research in Science and Technology (ISRST), USA and International Association of Engineers.

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Research interest includes Parallel and Distributed System, Grid and Cloud Computing, Mobile Computing and Evolutionary Computing.

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