Lu, Hongliang, Cao, Jiannong, Chawla, Shailey, Wang, Yuqi, Lv, Saohe, and Wang, Xiaodang (2016) A cluster based approach for task scheduling across multiple programming systems. In: Proceedings of the 15th International Symposium on Parallel and Distributed Computing. pp. 222-229. From: ISPDC 2016: 15th International Symposium on Parallel and Distributed Computing, 8-10 July 2016, Fuzhou, China.

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Abstract

As the data processing demands have been increasing, different types of data processing systems are being developed. The new programming systems have different characteristics like types of data handled, processing technique and performance. However, multiple new systems have introduced difficulties for non-expert users like choosing the right system and usage methodology of the new systems. In order to relieve the burden of common users of conducting data processing tasks and taking relevant advantage of the systems features, we intend to integrate the popular programming systems and provide more efficient data processing services. In this paper, we propose to address the task scheduling problem for integrating multiple programming systems. We have designed a cluster based approach for task scheduling across multiple programming systems. This approach helps in minimizing the makespan of workflows and resource consumption. The simulation results show that the proposed approach can reduce the resource consumption significantly while achieving a high makespan reduction for the workflows.

Item ID:	56217
Item Type:	Conference Item (Research - E1)
ISBN:	978-1-5090-4152-7
Keywords:	big data, task scheduling, data processing systems, cluster based, genetic algorithm
Date Deposited:	13 Dec 2018 02:10
FoR Codes:	46 INFORMATION AND COMPUTING SCIENCES > 4606 Distributed computing and systems software > 460605 Distributed systems and algorithms @ 100%
SEO Codes:	89 INFORMATION AND COMMUNICATION SERVICES > 8998 Environmentally Sustainable Information and Communication Services > 899899 Environmentally Sustainable Information and Communication Services not elsewhere classified @ 100%
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