SSUR: An Approach to Optimizing Virtual Machine Allocation Strategy Based on User Requirements for Cloud Data Center

• Published in: IEEE transactions on green communications and networking Vol. 5; no. 2; pp. 670 - 681
• Main Authors: Huang, Yuzhe, Xu, Huahu, Gao, Honghao, Ma, Xiaojin, Hussain, Walayat
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.06.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Cloud computing; Cloud data center; Computer centers; Data centers; Electronic devices; Energy consumption; Optimization; Prediction algorithms; Reliability; Resource management; Servers; Service centers; Shutdowns; Task analysis; throughput; User requirements; Virtual environments; VM allocation
• ISSN: 2473-2400; 2473-2400
• DOI: 10.1109/TGCN.2021.3067374

Cloud data centers provide services for an increasing number of applications. The virtual machines (VMs) that perform the corresponding application tasks need to be allocated to physical machines (PMs). For VM allocation, cloud service centers consider both energy consumption and quality of service (QoS), while cloud users are primarily concerned with their own needs, such as throughput and reliability. This paper proposes an allocation scheme for optimization based on user requirements in a cloud data center. First, various application requests from mobile phones and devices, which are regarded as a group of VM lists in the data center, are submitted to the cloud platform. Our method first allocates these arriving VMs to appropriate PMs based on their usage of hardware resources and the current throughput of the PMs in the data center. Second, due to dynamic workloads, the loads of the PMs that host these VMs may become very high. CPU utilization thresholds are set to determine whether migration is required, and the energy consumption before and after allocation is used to choose which VMs are reallocated. A suitable strategy for VM migration and PM shutdown can improve reliability and reduce energy consumption. Finally, it is shown through experimental simulations that compared with two existing algorithms, on the premise that the user requirements are met, the proposed method offers good performance in terms of total energy consumption, CPU utilization, number of PMs used and number of service-level agreement (SLA) violation.