Research on Mobility-Aware Computation Offloading and Resource Allocation Strategy

• Published in: Jisuanji gongcheng
• Main Author: Yuqi BAN, Liguo DUAN, Haoyu WEN, Aiping LI, Jumin ZHAO
• Format: Article
• Language: English
• Published: Editorial Office of Computer Engineering 2023-08-01
• Subjects: mobile edge computing(mec)|mobility-aware|computation offloading|resource allocation|offloading algorithm
• Online Access: https://www.ecice06.com/fileup/1000-3428/PDF/20230820.pdf

In Mobile Edge Computing(MEC), user equipment offloads computationally intensive tasks to edge servers for execution to reduce execution delay and energy consumption.This process requires 5G technology-based applications to support the high-speed movement of devices during computing.However, much of the current research on computational offload solutions is focused on static scenarios.To improve the quality of user experience, this study investigates a computational offloading scheme that considers device movement trajectories in MEC and thus more suitable multi-device and multi-MEC server scenarios.Because this scheme considers multiple factors such as device mobility, computing and communication resources, channel states, and mission requirements, it can be described as a mixed-integer nonlinear programming problem.To reduce the difficulties inherent in solving this problem, this study decomposes the problem into subproblems of offloading server selection, computing resource allocation, and subchannel selection under a fixed-server selection scheme.The convex optimization technique and improved Kuhn-Munkres algorithm are then used to solve the subproblems.This study also designs a heuristic offload server selection algorithm based on the solution to the subproblems and derives a suboptimal offload solution with polynomial time complexity.Simulations are conducted using the EdgeCloudSim tool, the results of which prove the effectiveness of the proposed algorithm as compared with five other commonly used offloading algorithms.The experimental results show that the average system utility gap between the algorithm and exhaustive algorithm can be controlled to within 2.3% when it meets the real-time requirements of a given task.