Customer Satisfaction-Aware Scheduling for Utility Maximization on Geo-distributed Cloud Data Centers

Abstract

With the increasingly growing amount of service requests from the world-wide customers, the cloud systems is capable of providing services while meeting the customers' satisfaction. Recently, to achieve the better reliability and performance, the cloud systems has been largely depending on the geographically distributed data centers. Nevertheless, the dollar cost of service placement by service providers (SP) differ from the multiple regions. Accordingly, it is crucial to design a request dispatching and resource allocation algorithm to maximize net profit. The existing algorithms are either built upon energy-efficient schemes alone, or multi-type requests and customer satisfaction oblivious. They cannot be applied to multi-type requests and customer satisfaction-aware algorithm design with the objective of maximizing net profit. This paper proposes a customer satisfaction-aware algorithm based on the Ant-Colony Optimization (AMP) for geo-distributed data centers. By introducing the model of customer satisfaction, we formulate the utility (or net profit) maximization issue as an optimization problem under the constraints of customer satisfaction and data centers. AMP maximizes SP net profit by dispatching service requests to the proper data centers and generating the appropriate amount of Virtual Machines (VMs) to meet customer satisfaction. To evaluate the proposed algorithm, we have conducted the comprehensive simulation and compared with the other state-of-the-art algorithms. Conclusive results have demonstrated the effectiveness of AMP both in small and large scale problem.