A Virtual Machine Scheduling Strategy with a Speed Switch and a Multi-Sleep Mode in Cloud Data Centers
| Autor: | Shunfu Jin, Shanshan Hao, Xiuchen Qie, Wuyi Yue |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Queueing theory
021103 operations research Markov chain Computer science Real-time computing 0211 other engineering and technologies 02 engineering and technology Energy consumption computer.software_genre Scheduling (computing) Control and Systems Engineering Virtual machine Search algorithm 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing computer Sleep mode Information Systems Efficient energy use |
| Zdroj: | Journal of Systems Science and Systems Engineering. 28:194-210 |
| ISSN: | 1861-9576 1004-3756 |
| Popis: | With the rapid growth of energy costs and the constant promotion of environmental standards, energy consumption has become a significant expenditure for the operating and maintaining of a cloud data center. To improve the energy efficiency of cloud data centers, in this paper, we propose a Virtual Machine (VM) scheduling strategy with a speed switch and a multi-sleep mode. In accordance with the current traffic loads, a proportion of VMs operate at a low speed or a high speed, while the remaining VMs either sleep or operate at a high speed. Commensurate with our proposal, we develop a continuous-time queueing model with an adaptive service rate and a partial synchronous vacation. We construct a two dimensional Markov chain based on the total number of requests in the system and the state of all the VMs. Using a matrix geometric solution, we mathematically estimate the energy saving level and the response performance of the system. Numerical experiments with analysis and simulation show that our proposed VM scheduling strategy can effectively reduce the energy consumption without significant degradation in response performance. Additionally, we establish a system utility function to trade off the different performance measures. In order to determine the optimal sleep parameter and the maximum system utility function, we develop an improved Firefly intelligent searching Algorithm. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full text from SpringerLink