Memory-aware resource management algorithm for low-energy cloud data centers

Abstract

The continuous advancement of cloud computing technology has driven the vigorous development of cloud data centers. This manifests itself not only in increasing numbers, but also in rapid expansion scale. At the same time, the problems of high energy consumption, high cost and high carbon emissions began to stand out. These factors have become a bottleneck restricting the further development of cloud computing technology. As a result of the application of virtualization technology, mature cloud service providers use virtual machines instead of physical machines to provide users with computing, storage and other services. Therefore, the scheduling algorithm of cloud tasks and virtual machines has been widely studied by academia as a core problem. However, most of the current cloud data center resource management algorithms take CPU utilization as the main consideration, and increase the CPU utilization through virtual machine integration to reduce the energy consumption of cloud data centers. However, these algorithms will cause waste and low utilization of other resources in the cloud data center due to excessive consideration of CPU utilization. This paper systematically analyzes the mapping relationship between cloud tasks, virtual machines and physical machines. At the same time, the performance of cloud tasks, virtual machines, and physical machines is modeled in the cloud data center. By comprehensively considering the CPU and memory characteristics of cloud tasks, the memory priority cloud task mapping rule is established. Based on the rule, the memory-aware resource management algorithm for low-energy cloud data centers (MALE) is proposed. The algorithm maps cloud tasks and deploys virtual machines according to the memory requirements of the cloud tasks, thereby achieving the goal of simultaneously reducing the total fee of cloud users and the energy consumption of cloud data centers. Finally, the algorithm is compared with the other three algorithms. The experimental simulation results show that the effect of the proposed algorithm in this paper is significantly better than the comparison algorithm for the total fee of cloud users and the energy consumption of the cloud data center.