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Energy-Aware Heuristic Scheduling Using Bin Packing MapReduce Scheduler for Heterogeneous Workloads Performance in Big Data. ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2022. [DOI: 10.1007/s13369-022-06963-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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Seethalakshmi V, Govindasamy V, Akila V. Real-coded multi-objective genetic algorithm with effective queuing model for efficient job scheduling in heterogeneous Hadoop environment. JOURNAL OF KING SAUD UNIVERSITY - COMPUTER AND INFORMATION SCIENCES 2022. [DOI: 10.1016/j.jksuci.2020.08.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
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Energy-Aware Task Scheduling Using Hybrid Firefly-BAT (FFABAT) in Big Data. CYBERNETICS AND INFORMATION TECHNOLOGIES 2018. [DOI: 10.2478/cait-2018-0031] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Abstract
In modern times there is an increasing trend of applications for handling Big data. However, negotiating with the concepts of the Big data is an extremely difficult issue today. The MapReduce framework has been in focus recently for serious consideration. The aim of this study is to get the task-scheduling over Big data using Hadoop. Initially, we prioritize the tasks with the help of k-means clustering algorithm. Then, the MapReduce framework is employed. The available resource is optimally selected using optimization technique in map-phase. The proposed method uses the FireFly Algorithm and BAT algorithms (FFABAT) for choosing the optimal resource with minimum cost value. The bat-inspired algorithm is a meta-heuristic optimization method developed by Xin-She Yang (2010). This bat algorithm is established on the echo-location behaviour of micro-bats with variable pulse rates of emission and loudness. Finally, the tasks are scheduled with the optimal resource in reducer-phase and stored in the cloud. The performance of the algorithm is analysed, based on the total cost, time and memory utilization.
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