Energy and cost-aware virtual machine consolidation in cloud computing

摘要

Cloud computing has become an essential part of the computational world, offering a variety of server capabilities as scalable virtualized services. Big data centers that deliver cloud computing services contain thousands of computational nodes that consume a significant amount of energy. By introducing the virtual machine (VM), virtualization technology is trying to overcome this problem. One impressive technique for minimizing the total number of active physical servers that lead to improved energy consumption is VM consolidation. To optimize the consolidation process, effective VM placement can be used. In this paper, we first present a mathematical model aimed at reducing power consumption and costs by employing an effective VM consolidation in the cloud data center. Subsequently, we propose a genetic algorithm-based meta-heuristic algorithm, namely, energy and cost-aware VM consolidation for resolving the problem. Finally, we compare our proposed model with the well-known first fit, first fit decreasing, and permutation pack algorithms. The experimental results show that our proposed model reduced power consumption and costs when compared with the three demonstrated algorithms.