Revenue-optimal task scheduling and resource management for IoT batch jobs in mobile edge computing
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Revenue-optimal task scheduling and resource management for IoT batch jobs in mobile edge computing Jiwei Huang1,2
· Songyuan Li3 · Ying Chen4
Received: 31 August 2019 / Accepted: 16 January 2020 © Springer Science+Business Media, LLC, part of Springer Nature 2020
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. Keywords Internet of Things (IoT) · Mobile edge computing · Task scheduling · Resource management · Revenue-optimal
1 Introduction The Internet of. Things (IoT) is a promising technical field in recent years, which interconnects a variety of senors and 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 Ying Chen
[email protected] Jiwei Huang [email protected] Songyuan Li [email protected] 1
Department of Computer Science and Technology, China University of Petroleum, Beijing 102249, China
2
Beijing Key Laboratory of Petroleum Data Mining, China University of Petroleum, Beijing 102249, China
3
State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China
4
Computer School, Beijing Information Science and Technology University, Beijing 100101, China
other IoT devices over the network enabling themselves to cooperate with each other and achieve common goals [29]. With the increasing amount and types of IoT devices joining the network, several IoT technical requirements are newly put forward. Massive data generated by IoT devices at the frontend brings challenges for efficient information processing, especially for the particular scenario requiring real-time data handling (e.g., Internet of Vehicles [10]). Furthermore, much more intelligent and powerful processing capacity should be equipped at the edge, and thus helps to provide various IoT devices (e.g., smart phones, GPS traker, mobile camera, smart bands, etc.) with diverse services. Therefore, it necessitates a
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