Double auction and profit maximization mechanism for jobs with heterogeneous durations in cloud federations

• Published in: Journal of cloud computing : advances, systems and applications Vol. 10; no. 1; pp. 1 - 22
• Main Authors: Lu, Runhao, Liang, Yuning, Ling, Qing, Li, Changle, Wu, Weigang
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 12.06.2021; Springer Nature B.V; SpringerOpen
• Subjects: Auction; Cloud federation; Clouds; Computer Communication Networks; Computer Science; Computer System Implementation; Computer Systems Organization and Communication Networks; Federations; Information Systems Applications (incl.Internet); Job scheduling; Pricing; Provisioning; Resource management; Resource scheduling; Software Engineering/Programming and Operating Systems; Special Purpose and Application-Based Systems; Virtual environments; Virtual machine; Resource management; Cloud federation; Auction; Virtual machine; Job scheduling
• ISSN: 2192-113X; 2192-113X
• DOI: 10.1186/s13677-021-00249-3

By sharing resources with each other, different cloud providers in a cloud federation can exploit their diversity in resource configuration and operational cost so as to improve service performance. They should consider the strategy of resource pricing, job scheduling and server provisioning altogether to maximize their own interests. On the other hand, they need to efficiently trade the resources with a suitable mechanism, typically auction, so as to guarantee the participants’ profits. Nevertheless, in consideration of the heterogeneous execution times of jobs, both the pricing strategy and trading mechanism should be delicately designed, which is obviously a challenging task. In this paper, we firstly propose a truthful, individual-rational and ex-post budget-balanced auction mechanism for selecting pairs of buyer and seller winners to trade virtual machines for different durations. Then, to maximize the individual profits, we propose a dynamic resource bidding scheme and a job scheduling strategy based on our importance model of jobs with heterogeneous execution times and resource requirements. The simulation results show that, compared with existing ones, our design can better handle varieties of both execution time and resource requirement and make the participants obtain more individual profits.