Residential College | false |
Status | 已發表Published |
Energy efficient VM scheduling strategies for HPC workloads in cloud data centers | |
Chandio,Aftab Ahmed1,2; Tziritas,Nikos1; Chandio,Muhammad Saleem2; Xu,Cheng Zhong1,3,4 | |
2019-12 | |
Source Publication | Sustainable Computing: Informatics and Systems |
ISSN | 2210-5379 |
Volume | 24 |
Abstract | In virtualized environments, virtual machine (VM) scheduling strategies incorporated with energy efficient techniques are needed to reduce the operational cost of the system while delivering high Quality of Service (QoS). It is widely accepted that the cost of the energy consumption in the environment is a dominant part of the owner's budget. However, when considering energy efficiency, VM scheduling decisions become more constrained, leading in the violation of job deadlines and hence compromising QoS. This paper studies energy efficient VM scheduling strategies in virtualized environments to minimize the queue time and makespan under the fulfillment of SLA requirements (i.e., deadline). Specifically, six energy efficient VM scheduling strategies are investigated incorporated with the dynamic voltage and frequency scaling (DVFS) power management technique. They consist of user-oriented and system-oriented policies. The strategies are extensively simulated and compared with three power management governing methods provided at hypervisor level (i.e., userspec, ondemand, and performance). To conduct simulation experiments, we employ real-world high performance computing (HPC) workloads collected from a production data center. For comparison and evaluation, we analyze the: (a) energy consumption, (b) runtime, (c) queue time, (d) makespan, and (e) slowdown ratio. Lastly, we highlight the strengths and weaknesses of the VM scheduling strategies that can help to choose the most appropriate VM scheduling strategy for a given scenario. |
Keyword | Cloud Computing Dvfs Energy Efficiency Hpc Cloud Hypervisor Power Management Schemes Vm Scheduling |
DOI | 10.1016/j.suscom.2019.100352 |
URL | View the original |
Language | 英語English |
WOS ID | WOS:000501803900001 |
Scopus ID | 2-s2.0-85072762971 |
Fulltext Access | |
Citation statistics | |
Document Type | Journal article |
Collection | Faculty of Science and Technology |
Corresponding Author | Chandio,Aftab Ahmed |
Affiliation | 1.Shenzhen Institutes of Advanced Technology,Chinese Academy of Sciences,Shenzhen,518055,China 2.Institute of Mathematics and Computer Science,University of Sindh,Jamshoro,70680,Pakistan 3.Department of Electrical and Computer Engineering,Wayne State University,Detroit,48202,United States 4.Department of Computer Science State Key Lab. of IoT for Smart City University of Macau,Macao SAR,China |
Recommended Citation GB/T 7714 | Chandio,Aftab Ahmed,Tziritas,Nikos,Chandio,Muhammad Saleem,et al. Energy efficient VM scheduling strategies for HPC workloads in cloud data centers[J]. Sustainable Computing: Informatics and Systems, 2019, 24. |
APA | Chandio,Aftab Ahmed., Tziritas,Nikos., Chandio,Muhammad Saleem., & Xu,Cheng Zhong (2019). Energy efficient VM scheduling strategies for HPC workloads in cloud data centers. Sustainable Computing: Informatics and Systems, 24. |
MLA | Chandio,Aftab Ahmed,et al."Energy efficient VM scheduling strategies for HPC workloads in cloud data centers".Sustainable Computing: Informatics and Systems 24(2019). |
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