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Managing overloaded hosts for energy-efficiency in cloud data centers

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Abstract

Traditional data centers are shifted toward the cloud computing paradigm. These data centers support the increasing demand for computational and data storage that consumes a massive amount of energy at a huge cost to the cloud service provider and the environment. Considerable energy is wasted to constantly operate idle virtual machines (VMs) on hosts during periods of low load. Dynamic consolidation of VMs from overloaded or underloaded hosts is an effective strategy for improving energy consumption and resource utilization in cloud data centers. The dynamic consolidation of VM from an overloaded host directly influences the service level agreements (SLAs), utilization of resources, and quality of service (QoS) delivered by the system. We proposed an algorithm, namely, GradCent, based on the Stochastic Gradient Descent technique. This algorithm is used to develop an upper CPU utilization threshold for detecting overloaded hosts by using a real CPU workload. Moreover, we proposed a dynamic VM selection algorithm called Minimum Size Utilization (MSU) for selecting the VMs from an overloaded host for VM consolidation. GradCent and MSU maintain the trade-off between energy consumption minimization and QoS maximization under specified SLA goal. We used the CloudSim simulations with real-world workload traces from more than a thousand PlanetLab VMs. The proposed algorithms minimized energy consumption and SLA violation by 23% and 27.5% on average, compared with baseline schemes, respectively.

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Acknowledgements

The National Key Research and Development Plan under Grant No. 2017YFB0801801, the National Natural Science Foundation of China (NSFC) under Grant No. 61672186, 61872110, support this work. Corresponding author is Professor Weizhe Zhang.

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Authors and Affiliations

  1. School of Computer Science and Technology, Harbin Institute of Technology, Harbin, 150001, China

    Rahul Yadav

  2. Harbin Institute of Technology, and with Cyberspace Security Research Center, Peng Cheng Laboratory, Shenzhen, 518055, China

    Weizhe Zhang & Chuanyi Liu

  3. Department of Computer Science, State University of New York, New Paltz, NY, 12561, USA

    Keqin Li

  4. Department of Computer Science, Sindh Madressatul Islam University, Karachi, Pakistan

    Asif Ali Laghari

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  1. Rahul Yadav
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  2. Weizhe Zhang
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Correspondence to Rahul Yadav.

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Yadav, R., Zhang, W., Li, K. et al. Managing overloaded hosts for energy-efficiency in cloud data centers. Cluster Comput 24, 2001–2015 (2021). https://doi.org/10.1007/s10586-020-03182-3

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