The GridEcon Platform: A Business Scenario Testbed for Commercial Cloud Services

Risch, Marcel; Altmann, Jörn; Guo, Li; Fleming, Alan; Courcoubetis, Costas

doi:10.1007/978-3-642-03864-8_4

Cited by 30 publications

(19 citation statements)

References 9 publications

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“…Several research projects have discussed the implementation of system resource markets [1], [14], [31]- [33], [38]. GRACE developed a market architecture for grid markets and outlined a market mechanism, while the good itself (i.e., computing resource) has not been defined [14].…”

Section: Cloud Markets In Researchmentioning

confidence: 99%

“…GridEcon proposed a commodity market for cloud computing services [1], [38]. Although an explicit service level agreement for standardized cloud services [37], the cloud service requirements, and the requirements for trading have been defined and specified, the issue of adaptation of standardized goods has not been addressed.…”

Section: Cloud Markets In Researchmentioning

confidence: 99%

“…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").…”

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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%

Section: Cloud Markets In Researchmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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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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“…They showed by statistical analysis that when there is a large number of resources and users, the auction can obtain an efficient allocation and a reasonable revenue for the cloud provider. Risch et al [7] designed a cloud services testbed to test cloud mechanisms. Altmann et al [8] designed an exchange market for grid resources, which was tested on the platform designed by Risch et al [7].…”

Section: Introductionmentioning

confidence: 99%

An Online Mechanism for Dynamic VM Provisioning and Allocation in Clouds

Zaman

Grosu

2012

2012 IEEE Fifth International Conference on Cloud Computing

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Abstract-Current cloud computing providers allocate their virtual machine (VM) instances via fixed price-based or auctionlike mechanisms. However, these mechanisms have one limitation, they are all offline mechanisms, therefore they need to collect information and be invoked periodically. In this paper, we address this limitation by designing an online mechanism for dynamic provisioning and allocation of VM instances in clouds. Our proposed mechanism, MOVMPA, is invoked as soon as a user places a request or some VM instances already allocated become available again. When invoked, the mechanism selects users who would be allocated VM instances for the period they requested for, and ensures that those users will continue using those VMs for the entire period requested. We prove that the mechanism is incentive compatible and also investigate its performance through extensive simulation experiments.

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