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Future Internet 2018, 10(6), 52; https://doi.org/10.3390/fi10060052

A Novel Self-Adaptive VM Consolidation Strategy Using Dynamic Multi-Thresholds in IaaS Clouds

1
School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China
2
Shanghai Key Laboratory of Computer Software Testing and Evaluating, Shanghai 201112, China
3
Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai 200444, China
*
Author to whom correspondence should be addressed.
Received: 11 May 2018 / Revised: 31 May 2018 / Accepted: 11 June 2018 / Published: 13 June 2018
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Abstract

With the rapid development of cloud computing, the demand for infrastructure resources in cloud data centers has further increased, which has already led to enormous amounts of energy costs. Virtual machine (VM) consolidation as one of the important techniques in Infrastructure as a Service clouds (IaaS) can help resolve energy consumption by reducing the number of active physical machines (PMs). However, the necessity of considering energy-efficiency and the obligation of providing high quality of service (QoS) to customers is a trade-off, as aggressive consolidation may lead to performance degradation. Moreover, most of the existing works of threshold-based VM consolidation strategy are mainly focused on single CPU utilization, although the resource request on different VMs are very diverse. This paper proposes a novel self-adaptive VM consolidation strategy based on dynamic multi-thresholds (DMT) for PM selection, which can be dynamically adjusted by considering future utilization on multi-dimensional resources of CPU, RAM and Bandwidth. Besides, the VM selection and placement algorithm of VM consolidation are also improved by utilizing each multi-dimensional parameter in DMT. The experiments show that our proposed strategy has a better performance than other strategies, not only in high QoS but also in less energy consumption. In addition, the advantage of its reduction on the number of active hosts is much more obvious, especially when it is under extreme workloads. View Full-Text
Keywords: self-adaptive VM consolidation; dynamic multi-thresholds; energy consumption; QoS; IaaS clouds self-adaptive VM consolidation; dynamic multi-thresholds; energy consumption; QoS; IaaS clouds
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Xie, L.; Chen, S.; Shen, W.; Miao, H. A Novel Self-Adaptive VM Consolidation Strategy Using Dynamic Multi-Thresholds in IaaS Clouds. Future Internet 2018, 10, 52.

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