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Revenue-optimal task scheduling and resource management for IoT batch jobs in mobile edge computing

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Abstract

With the growing prevalence of Internet of Things (IoT) devices and technology, a burgeoning computing paradigm namely mobile edge computing (MEC) is delicately proposed and designed to accommodate the application requirements of IoT scenario. In this paper, we focus on the problems of dynamic task scheduling and resource management in MEC environment, with the specific objective of achieving the optimal revenue earned by edge service providers. While the majority of task scheduling and resource management algorithms are formulated by an integer programming (IP) problem and solved in a dispreferred NP-hard manner, we innovatively investigate the problem structure and identify a favorable property namely totally unimodular constraints. The totally unimodular property further helps to design an equivalent linear programming (LP) problem which can be efficiently and elegantly solved at polynomial computational complexity. In order to evaluate our proposed approach, we conduct simulations based on real-life IoT dataset to verify the effectiveness and efficiency of our approach.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (No. 61972414), Beijing Natural Science Foundation (No.4202066) and the Fundamental Research Funds for the Central Universities (No. 2462018YJRC040).

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Authors and Affiliations

  1. Department of Computer Science and Technology, China University of Petroleum, Beijing, 102249, China

    Jiwei Huang

  2. Beijing Key Laboratory of Petroleum Data Mining, China University of Petroleum, Beijing, 102249, China

    Jiwei Huang

  3. State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, 100876, China

    Songyuan Li

  4. Computer School, Beijing Information Science and Technology University, Beijing, 100101, China

    Ying Chen

Authors
  1. Jiwei Huang
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  2. Songyuan Li
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  3. Ying Chen
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Correspondence to Ying Chen.

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This article is part of the Topical Collection: Special Issue on Emerging Trends on Data Analytics at the Network Edge

Guest Editors: Deyu Zhang, Geyong Min, and Mianxiong Dong

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Huang, J., Li, S. & Chen, Y. Revenue-optimal task scheduling and resource management for IoT batch jobs in mobile edge computing. Peer-to-Peer Netw. Appl. 13, 1776–1787 (2020). https://doi.org/10.1007/s12083-020-00880-y

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