Computation Offloading Strategy Optimization with Multiple Heterogeneous Servers in Mobile Edge Computing

• Published in: IEEE transactions on sustainable computing p. 1
• Main Author: Li, Keqin
• Format: Journal Article
• Language: English
• Published: IEEE 2024
• Subjects: Average response time; Cloud computing; computation offloading strategy; cost-performance ratio; Minimization; mobile edge cloud; mobile edge computing; Mobile handsets; Optimization; power consumption; power-performance tradeoff; queueing model; Servers; Task analysis; Time factors
• DOI: 10.1109/TSUSC.2019.2904680
• ISSN: 2377-3782

Computation offloading from a user equipment (UE) to a mobile edge cloud (MEC) is an effective way to ease the computational burden of mobile devices, to improve the performance of mobile applications, to reduce the energy consumption and to extend the battery lifetime of mobile user equipments. In this paper, we consider computation offloading strategy optimization with multiple heterogeneous servers in mobile edge computing. Queueing models are established for a UE and multiple heterogeneous servers from different MECs, and the average task response time of the UE and each MEC server and the average response time of all offloadable and non-offloadable tasks generated on the UE are rigorously analyzed. Three multi-variable optimization problems are formulated, i.e., minimization of average response time with average power consumption constraint, minimization of average power consumption with average response time constraint, and minimization of cost-performance ratio, so that computation offloading strategy optimization, power-performance tradeoff, as well as power-time product can all be studied in the context of load balancing. An efficient numerical method (which consists of a series of fast numerical algorithms) is developed to solve the problems of minimization of average response time with average power consumption constraint, minimization of average power consumption with average response time constraint, and minimization of cost-performance ratio. Numerical examples and data are also demonstrated to show the effectiveness of our method and to show the power-performance tradeoff, the power-time product, and the impact of various parameters. To the best of the author's knowledge, this is the first work in the literature that analytically addresses computation offloading strategy optimization with multiple heterogeneous servers in mobile edge computing.