Energy-Saving Computation Offloading by Joint Data Compression and Resource Allocation for Mobile-Edge Computing

• Published in: IEEE communications letters Vol. 23; no. 4; pp. 704 - 707
• Main Authors: Xu, Ding, Li, Qun, Zhu, Hongbo
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.04.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Cloud computing; Computation offloading; Computational geometry; Computer simulation; Convexity; Data compression; Edge computing; Energy conservation; Energy consumption; energy efficient; Mobile computing; Mobile-edge computing; Optimization; Resource allocation; Resource management; Servers; Task analysis; task offloading; Wireless sensor networks
• ISSN: 1089-7798; 1558-2558
• DOI: 10.1109/LCOMM.2019.2897630

In this letter, we consider a multiuser mobile-edge computing (MEC) system with latency constraint. In order to meet the latency requirement and save energy consumption, each user can partially offload the task to the MEC server for edge computing. Data compression is applied to compress the offloaded data before transmission to reduce the data size. The problem of jointly optimizing computation offloading, data compression and resource allocation to minimize energy consumption under the latency constraint and finite MEC computation capacity is considered. We transform the non-convex problem into a convex one and apply convex optimization to solve it. The simulation results demonstrate that our proposed scheme significantly outperforms the benchmark schemes.