Issue No. 06 - June (2018 vol. 29)
Xiang Li , Department of Computer Science and Technology, Zhejiang University, Hangzhou, China
Peter Garraghan , School of Computing & Communications, Lancaster University, LA, United Kingdom
Xiaohong Jiang , Department of Computer Science and Technology, Zhejiang University, Hangzhou, China
Zhaohui Wu , Department of Computer Science and Technology, Zhejiang University, Hangzhou, China
Jie Xu , School of Computing, University of Leeds, Leeds, United Kingdom
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.
Servers, Cooling, Computational modeling, Cloud computing, Processor scheduling, Virtual machining, Energy consumption
X. Li, P. Garraghan, X. Jiang, Z. Wu and J. Xu, "Holistic Virtual Machine Scheduling in Cloud Datacenters towards Minimizing Total Energy," in IEEE Transactions on Parallel & Distributed Systems, vol. 29, no. 6, pp. 1317-1331, 2018.