A genetic: algorithm approach to cost-based multi-QoS job scheduling in cloud computing environment
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Job scheduling system problem is a core and challenging issue in Cloud Computing. How to use Cloud computing resources efficiently and gain the maximum profits with job scheduling system is one of the Cloud computing service providers’ ultimate goals. In this paper, firstly, by analysis the differentiated QoS requirements of Cloud computing resources users’ jobs, we build the corresponding non-preemptive priority M/G/1 queuing model for the jobs. Then, considering Cloud computing service providers’ destination which is to gain the maximum profits by offering Cloud computing resources, webuilt the system cost function for this queuing model. After that, based on the queuing model and system cost function, considering the goals of both the Cloud Computing service users and providers, we gave the corresponding strategy and algorithm to get the approximate optimistic value of service for each job in the corresponding no-preemptive priority M/G/1 queuing model. Finally, we also provide corresponding simulations and numeral results. Analysis and number results show that our approach for job scheduling system can not only guarantee the QoS requirements of the users’ jobs, but also can make the maximum profits for the Cloud computing service providers.