2013/08/04 by Jian Zhao, Chuan Wu, Zhao, Jian +3
Computer Science · #Blockchain Technology Applications and Security #Cloud Computing and Resource Management #IoT and Edge/Fog Computing #cs.GT #cs.NI
paper · pdf · doi:10.48550/arxiv.1308.0841
14 pages
arxiv created 2013/12/08 · arxiv updated 2013/12/10
IaaS clouds invest substantial capital in operating their data centers. Reducing the cost of resource provisioning, is their forever pursuing goal. Computing resource trading among multiple IaaS clouds provide a potential for IaaS clouds to utilize cheaper resources to fulfill their jobs, by exploiting the diversities of different clouds' workloads and operational costs. In this paper, we focus on studying the IaaS clouds' cost reduction through computing resource trading among multiple IaaS clouds. We formulate the global cost minimization problem among multiple IaaS clouds under cooperative scenario where each individual cloud's workload and cost information is known. Taking into consideration jobs with disparate lengths, a non-preemptive approximation algorithm for leftover job migration and new job scheduling is designed. Given to the selfishness of individual clouds, we further design a randomized double auction mechanism to elicit clouds' truthful bidding for buying or selling virtual machines. We evaluate our algorithms using trace-driven simulations.