MapReduce scheduling algorithms: a review

Hashem, Ibrahim Abaker Targio and Nor Badrul, Anuar and Marjani, Mohsen and Ahmed, Ejaz and Chiroma, Haruna and Ahmad Firdaus, Zainal Abidin and Muhamad Taufik, Abdullah and Faiz, Alotaibi and Mahmoud Ali, Waleed Kamaleldin and Yaqoob, Ibrar and Abdullah, Gani (2020) MapReduce scheduling algorithms: a review. Journal of Supercomputing, 76 (7). pp. 4915-4945. ISSN 0920-8542. (Published)

Preview	Pdf MapReduce scheduling algorithms- a review.pdf Download (155kB) | Preview
	Pdf MapReduce scheduling algorithms-a review_FULL.pdf Restricted to Repository staff only Download (738kB) | Request a copy

Abstract

Recent trends in big data have shown that the amount of data continues to increase at an exponential rate. This trend has inspired many researchers over the past few years to explore new research direction of studies related to multiple areas of big data. The widespread popularity of big data processing platforms using MapReduce framework is the growing demand to further optimize their performance for various purposes. In particular, enhancing resources and jobs scheduling are becoming critical since they fundamentally determine whether the applications can achieve the performance goals in different use cases. Scheduling plays an important role in big data, mainly in reducing the execution time and cost of processing. This paper aims to survey the research undertaken in the field of scheduling in big data platforms. Moreover, this paper analyzed scheduling in MapReduce on two aspects: taxonomy and performance evaluation. The research progress in MapReduce scheduling algorithms is also discussed. The limitations of existing MapReduce scheduling algorithms and exploit future research opportunities are pointed out in the paper for easy identification by researchers. Our study can serve as the benchmark to expert researchers for proposing a novel MapReduce scheduling algorithm. However, for novice researchers, the study can be used as a starting point.

Item Type:	Article
Additional Information:	Indexed by Scopus
Uncontrolled Keywords:	Big data; Hadoop; MapReduce; Cloud computing; scheduling algorithms
Subjects:	Q Science > QA Mathematics > QA76 Computer software
Faculty/Division:	Faculty of Computer System And Software Engineering
Depositing User:	Mrs Norsaini Abdul Samat
Date Deposited:	14 Oct 2021 07:14
Last Modified:	14 Oct 2021 07:14
URI:	http://umpir.ump.edu.my/id/eprint/30281
Download Statistic:	View Download Statistics

Actions (login required)

View Item