An optimal resource provision policy in cloud computing based on customer profiles

Abstract

Conventional resource provision algorithms focus on how to maximize resource utilization and meet a fixed constraint of response time which be written in service level agreement (SLA). Unfortunately, the expected response time is highly variable and it is usually longer than the value of SLA. So, it leads to a poor resource utilization and unnecessary servers migration. We develop a framework for customer-driven dynamic resource allocation in cloud computing. Termed CDSMS (customer-driven service manage system), and the framework’s contributions are twofold. First, it can reduce the total migration times by adjusting the value of parameters of response time dynamically according to customers’ profiles. Second, it can choose a best resource provision algorithm automatically in different scenarios to improve resource utilization. Finally, we perform a serious experiment in a real cloud computing platform. Experimental results show that CDSMS provides a satisfactory solution for the prediction of expected response time and the interval period between two tasks and reduce the total resource usage cost.