摘要：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.
© 2020, Springer Science+Business Media, LLC, part of Springer Nature.

ISSN号：1936-6442

卷、期、页：卷13 期5 :1776-1787 特刊: SI

发表日期：2020-09-01

期刊分区（SCI为中科院分区）：三区

收录情况：SCIE（科学引文索引网络版）,EI（工程索引）

发表期刊名称：Peer-to-Peer Networking and Applications

参与作者：李松远,陈莹

第一作者：黄霁崴

论文类型：期刊论文

论文概要：黄霁崴,李松远,陈莹，Revenue-optimal task scheduling and resource management for IoT batch jobs in mobile edge computing，Peer-to-Peer Networking and Applications，2020，卷13 期5 :1776-1787 特刊: SI

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