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Multi-objective optimization of task assignment in distributed mobile edge computing

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Abstract

Traditional computing models and centralized cloud computing are not capable of meeting today’s application requirements, especially when deploying technologies, such as the Internet of things (IoT), 5G, and wearable devices, on a large scale. Mobile edge computing (MEC) introduces the feasibility of using edge and smart devices, such as gateways and smart phones, to perform task execution of different applications. Moreover, an efficient task scheduling approach should consider the deadlines requirements and the power consumption of the edge devices. This paper proposes a multi-objective optimization solution to assign different application tasks to different edge devices while minimizing the energy consumption of edge devices and the computation time of tasks. Task dependencies and data distribution are considered within a new and more general MEC model. Multi-objective evolutionary algorithm (MOEA) framework is used to solve the optimization problem subject to deadline and power consumption constraints. Results show that the proposed multi-objective approach achieves better performance in terms of energy and computation time when compared to a single objective approach.

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Authors and Affiliations

  1. Department of Computer Engineering, Jordan University of Science and Technology, P.O. Box 3030, Irbid, 22110, Jordan

    Sanaa Almasri & Moath Jarrah

  2. Department of Computer Networks and Security, Jordan University of Science and Technology, P.O. Box 3030, Irbid, 22110, Jordan

    Basheer Al-Duwairi

Authors
  1. Sanaa Almasri
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  2. Moath Jarrah
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  3. Basheer Al-Duwairi
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Correspondence to Moath Jarrah.

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Almasri, S., Jarrah, M. & Al-Duwairi, B. Multi-objective optimization of task assignment in distributed mobile edge computing. J Reliable Intell Environ 8, 21–33 (2022). https://doi.org/10.1007/s40860-021-00162-1

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  • DOI: https://doi.org/10.1007/s40860-021-00162-1

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