Please use this identifier to cite or link to this item: https://doi.org/10.1109/ICON.2011.6168522
Title: On the resource allocation and pricing strategies in Compute Clouds using bargaining approaches
Authors: Iyer, G.N.
Veeravalli, B. 
Keywords: axiomatic bargaining approaches
Cloud Computing
deadline
pricing
resource allocation
task finish time
Issue Date: 2011
Source: Iyer, G.N.,Veeravalli, B. (2011). On the resource allocation and pricing strategies in Compute Clouds using bargaining approaches. ICON 2011 - 17th IEEE International Conference on Networks : 147-152. ScholarBank@NUS Repository. https://doi.org/10.1109/ICON.2011.6168522
Abstract: In this paper, we consider addressing the resource allocation and pricing strategies in a Compute Cloud for both independent tasks and tasks from workflow schemes. Workflow scheduling of tasks is an important problem due to the fact that individual sub-tasks constituting the workflow may demand additional resources and hence may stall the entire process. We employ two axiomatic bargaining approaches (Nash Bargaining Solution (NBS) and Raiffa Bargaining Solution (RBS)) proposed in the literature to formulate the problem and derive an optimal solution for allocating virtual CPU instances in a Compute Cloud for both independent tasks and workflow tasks. We also analyze the effectiveness of our strategies via rigorous simulation experiments and we show that our strategies are adaptable to the requirements by the Cloud service providers (CSPs) in estimating the resource requirements. Further, we show that NBS ensures proportional fairness whereas RBS can handle real-time task arrivals and task dynamics. Finally we introduce the concept of asymmetric pricing scheme in which a user can specify his budget constraints and CSPs can attempt to maximize the revenue without sacrificing the performance. This asymmetric bargaining approach is an important contribution in this work which allows the CSP to choose different parameters such as deadline and/or budget requirements for deriving optimal resource allocation. The deadline based resource allocation is particularly useful for workflow-based applications which have tasks waiting for the completion of other tasks. © 2011 IEEE.
Source Title: ICON 2011 - 17th IEEE International Conference on Networks
URI: http://scholarbank.nus.edu.sg/handle/10635/71247
ISBN: 9781457718250
DOI: 10.1109/ICON.2011.6168522
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