Holistic Virtual Machine Scheduling in Cloud Datacenters towards Minimizing Total Energy
- 28 March 2017
- journal article
- research article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Parallel and Distributed Systems
- Vol. 29 (6), 1317-1331
- https://doi.org/10.1109/tpds.2017.2688445
Abstract
Energy consumed by Cloud datacenters has dramatically increased, driven by rapid uptake of applications and services globally provisioned through virtualization. By applying energy-aware virtual machine scheduling, Cloud providers are able to achieve enhanced energy efficiency and reduced operation cost. Energy consumption of datacenters consists of computing energy and cooling energy. However, due to the complexity of energy and thermal modeling of realistic Cloud datacenter operation, traditional approaches are unable to provide a comprehensive in-depth solution for virtual machine scheduling which encompasses both computing and cooling energy. This paper addresses this challenge by presenting an elaborate thermal model that analyzes the temperature distribution of airflow and server CPU. We propose GRANITE - a holistic virtual machine scheduling algorithm capable of minimizing total datacenter energy consumption. The algorithm is evaluated against other existing workload scheduling algorithms MaxUtil, TASA, IQR and Random using real Cloud workload characteristics extracted from Google datacenter tracelog. Results demonstrate that GRANITE consumes 4.3-43.6 percent less total energy in comparison to the state-of-the-art, and reduces the probability of critical temperature violation by 99.2 with 0.17 percent SLA violation rate as the performance penalty.Keywords
Funding Information
- National High Technology Research 863 Major Program of China (2011AA01A207)
- National Science Foundation of China (61272128)
- EPSRC (EP/P031617/1)
- program of China Scholarship (SCS)
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