Multi-objective scheduling of MapReduce jobs in big data processing

Hashem, Ibrahim Abaker Targio and Anuar, Nor Badrul and Marjani, Mohsen and Gani, Abdullah and Sangaiah, Arun Kumar and Sakariyah, Adewole Kayode (2018) Multi-objective scheduling of MapReduce jobs in big data processing. Multimedia Tools and Applications, 77 (8). pp. 9979-9994. ISSN 1380-7501, DOI

Full text not available from this repository.
Official URL:


Data generation has increased drastically over the past few years due to the rapid development of Internet-based technologies. This period has been called the big data era. Big data offer an emerging paradigm shift in data exploration and utilization. The MapReduce computational paradigm is a well-known framework and is considered the main enabler for the distributed and scalable processing of a large amount of data. However, despite recent efforts toward improving the performance of MapReduce, scheduling MapReduce jobs across multiple nodes has been considered a multi-objective optimization problem. This problem can become increasingly complex when virtualized clusters in cloud computing are used to execute a large number of tasks. This study aims to optimize MapReduce job scheduling based on the completion time and cost of cloud service models. First, the problem is formulated as a multi-objective model. The model consists of two objective functions, namely, (i) completion time and (ii) cost minimization. Second, a scheduling algorithm using earliest finish time scheduling that considers resource allocation and job scheduling in the cloud is proposed. Lastly, experimental results show that the proposed scheduler exhibits better performance than other well-known schedulers, such as FIFO and Fair.

Item Type: Article
Uncontrolled Keywords: Big data; Cloud computing; Hadoop; MapReduce; Scheduling algorithms
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Computer Science & Information Technology
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 08 Aug 2019 08:10
Last Modified: 08 Aug 2019 08:10

Actions (login required)

View Item View Item