Performance Framework for Virtual Machine Migration in Cloud Computing.

Alyas, Tahir; Ghazal, Taher M.; Alfurhood, Badria Sulaiman; Ahmad, Munir; Thawabeh, Ossma Ali; Alissa, Khalid; Abbas, Qaiser

In the cloud environment, the transfer of data from one cloud server to another cloud server is called migration. Data can be delivered in various ways, from one data centre to another. This research aims to increase the migration performance of the virtual machine (VM) in the cloud environment. VMs allow cloud customers to store essential data and resources. However, server usage has grown dramatically due to the virtualization of computer systems, resulting in higher data centre power consumption, storage needs, and operating expenses. Multiple VMs on one data centre manage share resources like central processing unit (CPU) cache, network bandwidth, memory, and application bandwidth. In multi-cloud, VM migration addresses the performance degradation due to cloud server configuration, unbalanced traffic load, resource load management, and fault situations during data transfer. VM migration speed is influenced by the size of the VM, the dirty rate of the running application, and the latency of migration iterations. As a result, evaluating VM migration performance while considering all of these factors becomes a difficult task. The main effort of this research is to assess migration problems on performance. The simulation results in Matlab show that if the VM size grows, the migration time of VMs and the downtime can be impacted by three orders of magnitude. The dirty page rate decreases, the migration time and the downtime grow, and the latency time decreases as network bandwidth increases during the migration time and post-migration overhead calculation when the VM transfer is completed. All the simulated cases of VMs migration were performed in a fuzzy inference system with performance graphs.

VIRTUAL machine systems; SERVER farms (Computer network management); CLOUD computing; CENTRAL processing units; COMPUTER systems; FUZZY logic

Computers, Materials & Continua, 2023, Vol 74, Issue 3, p6289

1546-2218

Academic Journal

10.32604/cmc.2023.035161