Data-Intensive Task Scheduling for Heterogeneous Big Data Analytics in IoT System

Xin Li, Liangyuan Wang, Jemal H. Abawajy, Xiaolin Qin, Giovanni Pau and Ilsun You
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Xin Li: College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210023, China
Liangyuan Wang: College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210023, China
Jemal H. Abawajy: School of Information Technology, Deakin University, Locked Bag 20000, Geelong, VIC 3220, Australia
Xiaolin Qin: College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210023, China
Giovanni Pau: The Faculty of Engineering and Architecture, Kore University of Enna, 94100 Enna, Italy
Ilsun You: Department of Information Security Engineering, Soonchunhyang University, Asan-si 31538, Korea

Energies, 2020, vol. 13, issue 17, 1-14

Abstract: Efficient big data analysis is critical to support applications or services in Internet of Things (IoT) system, especially for the time-intensive services. Hence, the data center may host heterogeneous big data analysis tasks for multiple IoT systems. It is a challenging problem since the data centers usually need to schedule a large number of periodic or online tasks in a short time. In this paper, we investigate the heterogeneous task scheduling problem to reduce the global task execution time, which is also an efficient method to reduce energy consumption for data centers. We establish the task execution for heterogeneous tasks respectively based on the data locality feature, which also indicate the relationship among the tasks, data blocks and servers. We propose a heterogeneous task scheduling algorithm with data migration. The core idea of the algorithm is to maximize the efficiency by comparing the cost between remote task execution and data migration, which could improve the data locality and reduce task execution time. We conduct extensive simulations and the experimental results show that our algorithm has better performance than the traditional methods, and data migration actually works to reduce th overall task execution time. The algorithm also shows acceptable fairness for the heterogeneous tasks.

Keywords: big data analysis; heterogeneous data-intensive task; IoT system; service response delay; task scheduling (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
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