A game theoretic formulation of the service provisioning problem in cloud systems

Ardagna, Danilo; Panicucci, Barbara; Passacantando, Mauro

doi:10.1145/1963405.1963433

Cited by 124 publications

(123 citation statements)

References 33 publications

(46 reference statements)

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“…Various resource-efficient and economically beneficent allocation techniques and methodologies have already been proposed to address these issues. These methodologies include game theoretical approaches [3], [45], stochastic programming [17], bio-inspired mechanisms [20], [41], auction-based algorithms [25], [36], [46] and agent-based approaches [21], [40], [44]. Most of these works perform QoS service selection and resource allocation based on some service performance indicators and economic indicators, including wait time optimization, utilization maximization and economic wastage minimization.…”

Section: Cloud Markets In Researchmentioning

confidence: 99%

Maximizing Liquidity in Cloud Markets through Standardization of Computational Resources

Breskovic

Brandić

Altmann

2013

2013 IEEE Seventh International Symposium on Service-Oriented System Engineering

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Abstract-Low liquidity in cloud markets can result in market instability and inefficiency, preventing the successful implementation of ubiquitous computing on demand. To circumvent this issue, it has been suggested to channel demand and supply into a limited number of standardized services. These standardized services can even be automatically adapted to user requirements with the goal of continuously improving market performance. In this paper, we focus on answering how many standardized services should be placed in the market. This work is based on a new definition of liquidity for cloud resources, which in turn has been derived from liquidity definitions of financial markets. Using a simulation framework, we evaluate our method for estimating the optimal quantity of standardized services with respect to market liquidity and demonstrate the benefits of this approach in terms of increase in market efficiency and decrease in users' cost of participation in the market. The methods presented in this paper have the potential to be applied in other electronic markets as well.Keywords-Service level agreement, electronic markets, cloud economics, autonomic computing, standardized goods, market modeling, market liquidity, IT services I. INTRODUCTION Common to utility computing, cloud computing and grid computing is that they offer computational resources (e.g., software, hardware and computing platform services) in a manner similar to utilities, such as water, electricity and telephony, without regard to where the services are hosted or how they are delivered [4], [15]. However, the current cloud market is fragmented and static, hindering the paradigm's ability to fulfill its promise of ubiquitous computing on demand and as a commodity. In order to address this issue, electronic markets for trading and/or allocating computational resources (i.e., grid and cloud services) have been proposed [1], [38].In cloud markets, computational services are described by service parameters and quality of service (QoS) objectives. Each setting of a service, i.e., configuration of parameters, desired values of QoS objectives, and combinations of these elements, defines a new service (also called "computational resource"). Due to a great number of available settings, markets suffer from a vast diversity and heterogeneity of computational resources.In addition to this, computational resources are characterized by a high dynamism and a high fungibility. Resource dynamism is a result of a large resource variability, a dynamic user base, diverse user behavior in the market, and the

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Section: Cloud Markets In Researchmentioning

confidence: 99%

Maximizing Liquidity in Cloud Markets through Standardization of Computational Resources

Breskovic

Brandić

Altmann

2013

2013 IEEE Seventh International Symposium on Service-Oriented System Engineering

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“…In (Ardagna et al 2011, Anselmi et al 2013 we used the GNE concept for a service provisioning problem where the perspective of SaaS providers hosting their applications at a single IaaS/PaaS provider is taken. Each SaaS needs to comply with end user applications Service Level Agreement (SLA) and, at the same time, maximize its own revenue, while minimizing the cost of use of resources supplied by the IaaS/PaaS.…”

Section: Introductionmentioning

confidence: 99%

Service Provisioning Problem in Cloud and Multi-Cloud Systems

Passacantando

Ardagna

Savi

2016

INFORMS Journal on Computing

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Cloud Computing is a new emerging paradigm that aims at streamlining the on-demand provisioning of resources as services, providing end-user with flexible and scalable services accessible through the Internet on a pay-per-use basis. Since modern Cloud systems operate in an open and dynamic world characterized by continuous changes, the development of efficient resource provisioning policies for Cloud-based services becomes increasingly challenging. This paper aims to study the hourly basis service provisioning problem through a generalized Nash game model. We take the perspective of SaaS (Software as a Service) providers which want to minimize the costs associated with the virtual machine instances allocated in a multi-IaaSs (Infrastructure as a Service) scenario, while avoiding incurring in penalties for requests execution failures and providing quality of service guarantees. SaaS providers compete and bid for the use of infrastructural resources, while the IaaSs want to maximize their revenues obtained providing virtualized resources.We propose a solution algorithm based on the best-reply dynamics, which is suitable for a distributed implementation. We demonstrate the effectiveness of our approach by performing numerical tests, considering multiple workloads and system configurations. Results show that our algorithm is scalable and provides significant cost savings with respect to alternative methods (5% on average but up to 260% for individual SaaS providers). Furthermore, varying the number of IaaS providers 8-15% cost savings can be achieved from the workload distribution on multiple IaaSs.

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“…In [10,11], we have considered a problem similar to the one faced here, analysing the service provisioning problem with on spot resources. With respect to our previous work, in this paper we extend the game-theoretic model considering a new and more realistic pricing model regulating SaaS and PaaS contract.…”

Section: Introductionmentioning

confidence: 99%

Generalized Nash equilibria for SaaS/PaaS Clouds

Anselmi

Ardagna

Passacantando

2014

European Journal of Operational Research

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Cloud computing is an emerging technology that allows to access computing resources on a pay-per-use basis. The main challenges in this area are the efficient performance management and the energy costs minimization.In this paper we model the service provisioning problem of Cloud Platform-as-aService systems as a Generalized Nash Equilibrium Problem and show that a potential function for the game exists. Moreover, we prove that the social optimum problem is convex and we derive some properties of social optima from the corresponding KarushKuhn-Tucker system. Next, we propose a distributed solution algorithm based on the best response dynamics and we prove its convergence to generalized Nash equilibria.Finally, we numerically evaluate equilibria in terms of their efficiency with respect to the social optimum of the Cloud by varying our algorithm initial solution. Numerical results show that our algorithm is scalable and very efficient and thus can be adopted for the run-time management of very large scale systems.

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A game theoretic formulation of the service provisioning problem in cloud systems

Cited by 124 publications

References 33 publications

Maximizing Liquidity in Cloud Markets through Standardization of Computational Resources

Maximizing Liquidity in Cloud Markets through Standardization of Computational Resources

Service Provisioning Problem in Cloud and Multi-Cloud Systems

Generalized Nash equilibria for SaaS/PaaS Clouds

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