Performance Analysis of a Dynamic Virtual Machine Management Method Based on the Power-Aware Integral Estimation
The latest cloud resource management research has revealed that virtual machine (VM) consolidation allows for effectively managing the physical resources of cloud data centers. However, a tremendous waste of power and physical resources has been pointed as one of the research challenges related to t...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | article |
| Language: | EN |
| Published: |
MDPI AG
2021
|
| Subjects: | |
| Online Access: | https://doaj.org/article/421632d5072a4516b31d56a8270dec76 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | The latest cloud resource management research has revealed that virtual machine (VM) consolidation allows for effectively managing the physical resources of cloud data centers. However, a tremendous waste of power and physical resources has been pointed as one of the research challenges related to the development of new methods for VM management in a cloud data center in order to deliver a wide range of IT services to clients effectively. This paper investigates a problem of power-aware VM consolidation under dynamic workloads, uncertainty, and a changing number of VMs. For this purpose, the authors propose a dynamic VM management method based on a beam search that takes into account four types of resources (CPU, memory, network throughput, and storage throughput) and six quality metrics. Optimal beam search algorithm parameters for the defined problem are determined using a new power-aware integral estimation method. The SLA violation minimization allows significant improvement of SLA quality metrics, accompanied by the decreased number of VM migrations and slight deterioration in the power consumption. The proposed method is evaluated using common widespread hardware configurations and Bitbrains workload traces. The experiments show that the proposed approach can ensure the efficient use of cloud resources in terms of SLA violation and the number of VM migrations. |
|---|