Correlation-Aware Virtual Machine Placement in Data Center Networks
| Autor: | Yongjian Wang, Guihai Chen, Tao Chen, Linghe Kong, Xiaofeng Gao, Yaoming Zhu |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
business.industry
Computer science Distributed computing 020206 networking & telecommunications Cloud computing 02 engineering and technology computer.software_genre 020202 computer hardware & architecture Virtual machine Service level Server 0202 electrical engineering electronic engineering information engineering Key (cryptography) Data center business computer Performance metric |
| Zdroj: | Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering ISBN: 9783319696041 |
| DOI: | 10.1007/978-3-319-69605-8_3 |
| Popis: | The resource utilization (CPU, memory) is a key performance metric in data center networks. The goal of the cloud platform supported by data center networks is achieving high average resource utilization while guaranteeing the quality of cloud services. Previous work focus on increasing the time-average resource utilization and decreasing the overload ratio of servers by designing various efficient virtual machine placement schemes. Unfortunately, most of virtual machine placement schemes did not involve the service level agreements and statistical methods. In this paper, we propose a correlation-aware virtual machine placement scheme that effectively places virtual machines on physical machines. First, we employ Neural Networks model to forecast the resource utilization trend according to the historical resource utilization data. Second, we design correlation-aware placement algorithms to enhance resource utilization while meeting the user-defined service level agreements. The results show that the efficiency of our virtual machine placement algorithms outperform the previous work by about 15%. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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