Generalized Nash equilibria for SaaS/PaaS Clouds

Anselmi, Jonatha; Ardagna, Danilo; Passacantando, Mauro

doi:10.1016/j.ejor.2013.12.007

Cited by 57 publications

(28 citation statements)

References 38 publications

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“…Constraint (7) ensures that no more than the available reserved VMs can be allocated. Constraint (8) guarantees that enough VMs are allocated to execute submitted jobs within their deadlines. Constraints (9) bound the job concurrency level for each user.…”

Section: Optimization Problemmentioning

confidence: 99%

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Optimal Map Reduce Job Capacity Allocation in Cloud Systems

Malekimajd

Ardagna

Ciavotta

et al. 2015

SIGMETRICS Perform. Eval. Rev.

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We are entering a Big Data world. Many sectors of our economy are now guided by data-driven decision processes. Big Data and business intelligence applications are facilitated by the MapReduce programming model while, at infrastructural layer, cloud computing provides flexible and cost effective solutions for allocating on demand large clusters. Capacity allocation in such systems is a key challenge to provide performance for MapReduce jobs and minimize cloud resource costs. The contribution of this paper is twofold: (i) we provide new upper and lower bounds for MapReduce job execution time in shared Hadoop clusters, (ii) we formulate a linear programming model able to minimize cloud resources costs and job rejection penalties for the execution of jobs of multiple classes with (soft) deadline guarantees. Simulation results show how the execution time of MapReduce jobs falls within 14% of our upper bound on average. Moreover, numerical analyses demonstrate that our method is able to determine the global optimal solution of the linear problem for systems including up to 1,000 user classes in less than 0.5 seconds.

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Section: Optimization Problemmentioning

confidence: 99%

“…First, the minimum cost for running a single job (let it be cj i ) is evaluated by setting H up i = H low i and solving problem (P 2), disabling the admission control mechanism. Then, we set the penalty value for job rejections p i = 10 cj i as in [8]. We varied H …”

Section: Design Of Experimentsmentioning

confidence: 99%

Optimal Map Reduce Job Capacity Allocation in Cloud Systems

Malekimajd

Ardagna

Ciavotta

et al. 2015

SIGMETRICS Perform. Eval. Rev.

Self Cite

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“…In this method, in order to offer each bid, the information of previous bids has also been used. Economic mechanisms are introduced as one of the main approaches of in resource management systems such as agents [1], telecommunication networks [17], data mining [18], cluster computing [19] and grid computing [20]. In these systems, numerous management instances, including bandwidth price, congestion control of TCP, content delivery and routing, are investigated and researched.…”

Section: In Public Environmentmentioning

confidence: 99%

Efficient Nash equilibrium resource allocation based on game theory mechanism in cloud computing by using auction

Nezarat

Dastghaibifard

2015

2015 1st International Conference on Next Generation Computing Technologies (NGCT)

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One of the most complex issues in the cloud computing environment is the problem of resource allocation so that, on one hand, the cloud provider expects the most profitability and, on the other hand, users also expect to have the best resources at their disposal considering the budget constraints and time. In most previous work conducted, heuristic and evolutionary approaches have been used to solve this problem. Nevertheless, since the nature of this environment is based on economic methods, using such methods can decrease response time and reducing the complexity of the problem. In this paper, an auction-based method is proposed which determines the auction winner by applying game theory mechanism and holding a repetitive game with incomplete information in a non-cooperative environment. In this method, users calculate suitable price bid with their objective function during several round and repetitions and send it to the auctioneer; and the auctioneer chooses the winning player based the suggested utility function. In the proposed method, the end point of the game is the Nash equilibrium point where players are no longer inclined to alter their bid for that resource and the final bid also satisfies the auctioneer's utility function. To prove the response space convexity, the Lagrange method is used and the proposed model is simulated in the cloudsim and the results are compared with previous work. At the end, it is concluded that this method converges to a response in a shorter time, provides the lowest service level agreement violations and the most utility to the provider.

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“…They proposed a utility coordination method to improve the comprehensive utility of the resource service transaction chain under the decentralized decision-making condition. Ardagna et al (2013) and Anselmi et al (2014) modeled the Nash equilibriu m of service provisioning prob lem in cloud system. Yang and Cai (2014) analy zed the Stackelberg equilibriu m between cloud storage provider and end user.…”

Section: Introductionmentioning

confidence: 99%

The equilibrium of cloud manufacturing system under cost uncertainty

Guo

Wei

Wang

2017

JAMDSM

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In this paper, we exp lore the equilibriu m p roblem of cloud manufacturing system (CM fgS) with cost uncertainty. We propose a CMfgS supernetwork model in wh ich decision-makers (resource service providers (RSPs), the agent and resource service demanders (RSDs)) seek to determine their optimal behavior in an environment with cost uncertainty. The cost uncertainty is represented by random variab les and the decision-makers' risk aversion attitude is modeled by adopting mean-variance utility. The equilibriu m conditions governing the supernetwork model are proposed based on variational inequalities. To investigate the impacts of decision-makers' risk aversion attitude and the degree of cost uncertainty on equilibriu m of CMfgS, we conduct a numerical analysis in this study. The results indicate that a risk averse RSP (or agent) tends to raise selling price to mitigate risk, and a risk averse RSD is willing to pay mo re fo r the needed services . We also show that a risk neutral RSP could sell more services when the variance of cost distribution is relativ e large but the opposite occurs for risk averse RSPs .Key words : Cloud manufacturing system, Equilibriu m, Cost uncertainty, Risk aversion, Variational inequalities IntroductionCloud manufacturing (CM fg), which was proposed in 2010, is an emerging service oriented manufacturing mode. The core idea of CMfg is manufacturing as a service, and the aim o f it is to realize the free sharing, h igh utilization, and on-demand use of manufacturing resources and capabilities through providing reliab le, on-demand, high quality, cost effective manufacturing services in a cloud manufacturing system (CMfgS) (Tao et al., 2011).In CMfgS, there are three kinds of ro les involved in service t ransactions: resource service provider (RSP), resource service demander (RSD), and the agent (also called as cloud operator). The RSPs o wn the manufacturing resources and abilities which are provided to the agent on demand. The RSDs are service consumers. They submit service requirements to the agent and gain optimal services fro m it. The agent is responsible fo r searching, co mb ining, coordinating and managing the required services for meeting RSDs' requirements. After a RSD's request is received by the agent, it searches in the database and propose a reliab le and optimal service scheme for the RSD. When the RSD mod ifies and confirms the service p lan, the agent sends service requests to the selected RSPs. then specific services are organized and launched to meet the RSD's needs. Finally, the service output is sent to the RSD (Wang and Xu, 2013). In this process, each participant is an independent decision-maker. CMfg S has the advantage of managing dynamic service resources in a systematic and integrated way and provid ing reliab le, on-demand, high quality services for RSDs. With the help of the agent's intelligent management, RSDS need not take much time to select suitable services and design service combination schemes. Though RSPs and RSDs must pay for the agent's mana...

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Generalized Nash equilibria for SaaS/PaaS Clouds

Cited by 57 publications

References 38 publications

Optimal Map Reduce Job Capacity Allocation in Cloud Systems

Optimal Map Reduce Job Capacity Allocation in Cloud Systems

Efficient Nash equilibrium resource allocation based on game theory mechanism in cloud computing by using auction

The equilibrium of cloud manufacturing system under cost uncertainty

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