Location-Aware Energy-Efficient Workload Allocation in Geo Distributed Cloud Environment
Soha Rawas and Ahmed Zekri
Journal of Computer Science
The proliferation of cloud computing relied on the virtualization of the compute and storage resources and provisioning them dynamically according to users’ needs on a pay-per-use model. Massive cloud providers have geo-distributed cloud data centers to ensure service reliability, availability and satisfy user’s need. Therefore, cloud management systems are necessary to increase the profit of cloud providers and to improve the quality-of service demanded by users. This paper focuses on an energy-efficient method to solve the problem of allocating data-intensive workloads in geographically distributed data centers. The workload’s tasks are characterized by large data transfer times than their execution times. The problem formulated as a nonlinear programming optimization problem. Then, to find an optimal solution to the problem, meta-heuristic genetic algorithm is proposed. The designed heuristic takes into account the cost of the data transfer time from the storage location to the compute servers as well as the workload makespan on the available hosts. Extensive simulations using the CloudSim simulator are conducted to evaluate the efficacy of the proposed allocation method and how it performs with respect to other methods in the literature. Our results show significant enhancements in energy consumption while respecting the user’s QoS.
© 2018 Soha Rawas and Ahmed Zekri. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.