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Sustainability 2017, 9(12), 2357;

Energy-Aware Cluster Reconfiguration Algorithm for the Big Data Analytics Platform Spark

Department of Computer and Information Science, University of Macau, Taipa 999078, Macau, China
School of Computer Science, North China University of Technology, Beijing 100144, China
Lab of Networks and Cybersecurity, Innopolis University, Innopolis 420500, Russia
School of Computer Science and Engineering, University of New South Wales, Sydney 2052, Australia
Authors to whom correspondence should be addressed.
Received: 18 October 2017 / Revised: 11 December 2017 / Accepted: 14 December 2017 / Published: 18 December 2017
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The development of Cloud computing and data analytics technologies has made it possible to process big data faster. Distributed computing schemes, for instance, can help to reduce the time required for data analysis and thus enhance its efficiency. However, fewer researchers have paid attention to the problem of the high-energy consumption of the cluster, placing a heavy burden on the environment, especially when the number of nodes is extremely large. As a consequence, the principle of sustainable development is violated. Considering this problem, this paper proposes an approach that can be applied to remove less-efficient nodes or to migrate over-utilized nodes of the cluster so as to adjust the load of the cluster properly and thereby achieve the goal of energy conservation. Furthermore, in order to testify the performance of the proposed methodology, we present the simulation results implemented by using CloudSim. View Full-Text
Keywords: cloud computing; sustainable computing; Spark cloud computing; sustainable computing; Spark

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Duan, K.; Fong, S.; Song, W.; Vasilakos, A.V.; Wong, R. Energy-Aware Cluster Reconfiguration Algorithm for the Big Data Analytics Platform Spark. Sustainability 2017, 9, 2357.

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