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

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Jiwei Huang, Songyuan Li, Ying Chen

Published: 2020, Last Modified: 21 Dec 2025Peer-to-Peer Netw. Appl. 2020EveryoneRevisionsBibTeXCC BY-SA 4.0
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.