Journal
JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING
Volume 121, Issue -, Pages 15-26Publisher
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.jpdc.2018.06.011
Keywords
Energy conservation; SLAV; Trade-off; Cloud data center; IaaS Cloud
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Funding
- National Nature Science Foundation of China [U1404622]
- Shanghai Innovation Action Plan Project [16511101200]
- Henan Science and Technology Innovation Support Plan [18A520054]
- Development Project of Henan Provincial Department of Science and Technology [162102310124]
- Cloud Technology Endowed Professorship
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Large-scale virtualized data centers are increasingly becoming the norm in our data-intensive society. One pressing challenge is to reduce energy consumption in such data centers for edge computing deployment, which would have flow-on effects on reducing the operating costs and carbon dioxide emissions. Dynamic virtual machine consolidation is an effective way to improve resource utilization and energy efficiency. In this paper, a comprehensive strategy is proposed, which is based on time-series forecasting approach. In this strategy, specific adjustment of threshold is applied to adapt the dynamic workload. We then use a real-world dataset (i.e. workload trace in Google) for evaluation, whose findings demonstrate that our strategy outperforms other benchmarks. (C) 2018 Elsevier Inc. All rights reserved.
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