Energy-Aware and Proactive Host Load Detection in Virtual Machine Consolidation
| Autor: | Vahid Ghods, Seyed Yahya Zahedi Fard, Mohammad Karim Sohrabi |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Service quality
business.industry Computer science Quality of service Cloud computing Energy consumption computer.software_genre Computer Science Applications Control and Systems Engineering Virtual machine Data center Resource management Electrical and Electronic Engineering business computer Host (network) Computer network |
| Zdroj: | Information Technology and Control. 50:332-341 |
| ISSN: | 2335-884X 1392-124X |
| DOI: | 10.5755/j01.itc.50.2.28056 |
| Popis: | With the expansion and enhancement of cloud data centers in recent years, increasing the energy consumptionand the costs of the users have become the major concerns in the cloud research area. Service quality parametersshould be guaranteed to meet the demands of the users of the cloud, to support cloud service providers,and to reduce the energy consumption of the data centers. Therefore, the data center's resources must be managedefficiently to improve energy utilization. Using the virtual machine (VM) consolidation technique is animportant approach to enhance energy utilization in cloud computing. Since users generally do not use all thepower of a VM, the VM consolidation technique on the physical server improves the energy consumption andresource efficiency of the physical server, and thus improves the quality of service (QoS). In this article, a serverthreshold prediction method is proposed that focuses on the server overload and server underload detectionto improve server utilization and to reduce the number of VM migrations, which consequently improves theVM's QoS. Since the VM integration problem is very complex, the exponential smoothing technique is utilizedfor predicting server utilization. The results of the experiments show that the proposed method goes beyondexisting methods in terms of power efficiency and the number of VM migrations. |
| Databáze: | OpenAIRE |
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