Key parameters decision for cloud computing: Insights from a multiple game model. (30th May 2017)

• Record Type: Journal Article
• Authors: Wang, Jiaze; Liu, Anfeng; Zhang, Shigeng
• Other Names: Bu Kai guestEditor.; Xiao Bin guestEditor.; Qian Yi guestEditor.; Dong Fang guestEditor.; Shen Jun guestEditor.
• Is Part Of: Concurrency and computation. Volume 29:Number 19(2017)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2017-05-30
• Subjects: cloud computing system -- equilibrium point -- key parameters decision -- multiple game model; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.4200
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: In service‐oriented cloud computing systems (CCSs), the aim of cloud service organizers (CSOs) is to achieve maximum profit by collecting metadata with low cost from big data reporters (BDRs) and to provide advanced services to customers at a high price. In these systems, BDRs receive payoffs by reporting metadata to CSOs and exchanging metadata with other BDRs, and customers expect to get high‐quality services at a low price. However, because the missing of a critical parameters decision model in such service‐oriented CCSs, it is difficult to measure key parameters in CCSs such as price and quality of services in the competitive market. In this paper, we propose a multiple game (MG) model to formulate the critical parameters decision process. In the MG model, there are multiple games: games among BDRs and games among CSOs under the rule of "survival of the fittest, " games between BDRs and CSOs under the rule of "the highest payoff first, " and games between customers and CSOs under the rule of "the lowest price and the highest quality of service (QoS) first." With the proposed multiple game (MG) model, the optimal key parameters can be obtained and the Pareto‐optimal equilibrium point can be achieved. Extensive simulation results demonstrate the effectiveness and efficiency of the proposed MG model in dynamically deciding key parameters in CCSs.