Customer satisfaction-aware scheduling for utility maximization on geo-distributed data centers

摘要

With the increasingly growing amount of service requests from the world-wide customers, the cloud systemsrnare capable of providing services while meeting the customers’ satisfaction. Recently, to achieve thernbetter reliability and performance, the cloud systems have been largely depending on the geographically distributedrndata centers. Nevertheless, the dollar cost of service placement by service providers (SP) differ fromrnthe multiple regions. Accordingly, it is crucial to design a request dispatching and resource allocation algorithmrnto maximize net profit. The existing algorithms are either built upon energy-efficient schemes alone,rnor multi-type requests and customer satisfaction oblivious. They cannot be applied to multi-type requestsrnand customer satisfaction-aware algorithm design with the objective of maximizing net profit. This paperrnproposes an ant-colony optimization-based algorithm for maximizing SP’s net profit (AMP) on geographicallyrndistributed data centers with the consideration of customer satisfaction. First, using model of customerrnsatisfaction, we formulate the utility (or net profit) maximization issue as an optimization problem underrnthe constraints of customer satisfaction and data centers. Second, we analyze the complexity of the optimalrnrequests dispatchment problem and rigidly prove that it is an NP-complete problem. Third, to evaluate thernproposed algorithm, we have conducted the comprehensive simulation and compared with the other stateof-rnthe-art algorithms. Also, we extend our work to consider the data center’s power usage effectiveness. Itrnhas been shown that AMP maximizes SP net profit by dispatching service requests to the proper data centersrnand generating the appropriate amount of virtual machines to meet customer satisfaction. Moreover, we alsorndemonstrate the effectiveness of our approach when it accommodates the impacts of dynamically arrivedrnheavy workload, various evaporation rate and consideration of power usage effectiveness.