@Article{cmc.2023.039076,
AUTHOR = {Pingping Li, Jiuxin Cao},
TITLE = {Virtual Machine Consolidation with Multi-Step Prediction and Affinity-Aware Technique for Energy-Efficient Cloud Data Centers},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {76},
YEAR = {2023},
NUMBER = {1},
PAGES = {81--105},
URL = {http://www.techscience.com/cmc/v76n1/53047},
ISSN = {1546-2226},
ABSTRACT = {Virtual machine (VM) consolidation is an effective way to improve
resource utilization and reduce energy consumption in cloud data centers.
Most existing studies have considered VM consolidation as a bin-packing
problem, but the current schemes commonly ignore the long-term relationship
between VMs and hosts. In addition, there is a lack of long-term consideration for resource optimization in the VM consolidation, which results in
unnecessary VM migration and increased energy consumption. To address
these limitations, a VM consolidation method based on multi-step prediction
and affinity-aware technique for energy-efficient cloud data centers (MPaAFVMC) is proposed. The proposed method uses an improved linear regression
prediction algorithm to predict the next-moment resource utilization of hosts
and VMs, and obtains the stage demand of resources in the future period
through multi-step prediction, which is realized by iterative prediction. Then,
based on the multi-step prediction, an affinity model between the VM and
host is designed using the first-order correlation coefficient and Euclidean
distance. During the VM consolidation, the affinity value is used to select the
migration VM and placement host. The proposed method is compared with
the existing consolidation algorithms on the PlanetLab and Google cluster
real workload data using the CloudSim simulation platform. Experimental
results show that the proposed method can achieve significant improvement
in reducing energy consumption, VM migration costs, and service level agreement (SLA) violations.},
DOI = {10.32604/cmc.2023.039076}
}