Joint Optimization of Task Offloading and Resource Allocation in Tactical Edge Networks

Oct 28, 2024· Zhaofeng Zhang , Xuanli Lin , Guoliang Xue , Yanchao Zhang , Kevin S. Chan · 1 min read
Summary
This paper jointly chooses task offloading and resource allocation in tactical edge networks to maximize completion rewards under computing and communication constraints. An exact search algorithm uses problem-specific pruning, while a simulated-annealing heuristic offers an efficient alternative. Numerical evaluations compare optimal solutions with the heuristic’s competitive performance.
Type
Publication
MILCOM 2024 - 2024 IEEE Military Communications Conference (MILCOM), 703-708. IEEE
publication

Abstract

Recent years have witnessed explosive growth in deploying IoT devices over tactical edge networks. Due to the limited computing resources of local edge devices, there is an urgent need to offload the computing tasks to edge servers. A central question here is “How can we design offloading strategies and resource allocation plans so that all the computing resource constraints of servers and communication constraints between devices and servers are satisfied?” In this paper, we formulate the problem of jointly optimizing task offloading and resource allocation (JOA) in tactical edge networks as maximizing the total task completion rewards subject to various resource constraints. We design two algorithms to solve the JOA problem. The first is an exact algorithm using a search tree, where the branch-cutting criterion is well-crafted based on the property of the JOA problem. The second is a meta-heuristic algorithm based on the simulated annealing approach. We conduct numerical evaluations and demonstrate that the proposed exact algorithm can obtain the optimal task offloading strategy and resource allocation plan. The heuristic algorithm can efficiently provide competitive performance.

Edge Network Task Offloading Joint Optimization
Xuanli Lin
Authors
Xuanli Lin
PhD Student in Computer Science

Xuanli Lin is a fifth-year PhD student in the Computer Science department at Arizona State University, supervised by Dr. Guoliang Xue.

His research interests include network optimization, network security, artificial intelligence, and the Internet of Things.

Guoliang Xue
Authors
Guoliang Xue
Professor of Computer Science and Engineering
Guoliang Xue is a professor in Arizona State University’s School of Computing and Augmented Intelligence and an IEEE Fellow. He investigates wireless and quantum networks, network security and privacy, and optimization. He earned his PhD in computer science from the University of Minnesota in 1991. His honors include the IEEE Communications Society’s 2019 William R. Bennett Prize, and he chaired the IEEE INFOCOM Steering Committee from 2020 through 2025.
Yanchao Zhang
Authors
Yanchao Zhang
Professor
Yanchao Zhang is a professor in the School of Electrical, Computer and Energy Engineering at Arizona State University. He directs the Cyber and Network Security Group and the DoD Center of Excellence in Future Generation Wireless Technology. His research addresses security and privacy in networked systems, including wireless and mobile networks, the Internet of Things, and cloud and edge computing. He earned his PhD from the University of Florida in 2006 and is an IEEE Fellow.
Kevin S. Chan
Authors
Kevin S. Chan
Lead Research Electronics Engineer
Kevin S. Chan is a lead research electronics engineer and Network Science Team Lead at the U.S. Army DEVCOM Army Research Laboratory. His research spans network science, edge computing, and cybersecurity. He holds a bachelor’s degree from Carnegie Mellon University and master’s and doctoral degrees in electrical and computer engineering from Georgia Tech. He received the IEEE Communications Society’s Leonard G. Abraham Prize in 2021.