A QoS-aware virtual machine scheduling method for energy conservation in cloud-based cyber-physical systems


Nowadays, with the development of cyber-physical systems (CPS), there are an increasing amount of applications deployed in the CPS to connect cyber space with physical world better and closer than ever. Furthermore, the cloud-based CPS bring massive computing and storage resource for CPS, which enables a wide range of applications. Meanwhile, due to the explosive expansion of applications deployed on the CPS, the energy consumption of the cloud-based CPS has received wide concern. To improve the energy efficiency in the cloud environment, the virtualized technology is employed to manage the resources, and the applications are generally hosted by virtual machines (VMs). However, it remains challenging to meet the Quality-of-Service (QoS) requirements. In view of this challenge, a QoS-aware VM scheduling method for energy conservation, named QVMS, in cloud-based CPS is designed. Technically, our scheduling problem is formalized as a standard multi-objective problem first. Then, the Non-dominated Sorting Genetic Algorithm III (NSGA-III) is adopted to search the optimal VM migration solutions. Besides, SAW (Simple Additive Weighting) and MCDM (Multiple Criteria Decision Making) are employed to select the most optimal scheduling strategy. Finally, simulations and experiments are conducted to verify the effectiveness of our proposed method.

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This research is supported by the National Science Foundation of China under grant no. 61702277 and no. 61872219.

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Correspondence to Xiaolong Xu.

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This article belongs to the Topical Collection: Special Issue on Smart Computing and Cyber Technology for Cyberization

Guest Editors: Xiaokang Zhou, Flavia C. Delicato, Kevin Wang, and Runhe Huang

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Qi, L., Chen, Y., Yuan, Y. et al. A QoS-aware virtual machine scheduling method for energy conservation in cloud-based cyber-physical systems. World Wide Web 23, 1275–1297 (2020). https://doi.org/10.1007/s11280-019-00684-y

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  • Cyber-physical systems
  • QoS
  • Energy conservation
  • VM scheduling
  • Cloud