Joint Task Offloading and Cache Placement for Energy-Efficient Mobile Edge Computing Systems

• Published in: IEEE wireless communications letters Vol. 12; no. 4; p. 1
• Main Authors: Liang, Jingxuan, Xing, Hong, Wang, Feng, Lau, Vincent K. N.
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.04.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Arrivals; Benchmark testing; branch-and-bound; Caching; Computation offloading; Edge computing; Energy consumption; Internet of Things; Mixed integer; Mobile computing; Mobile edge computing; Optimization; Placement; proactive cache placement; Servers; Task analysis; Task scheduling; Vehicle dynamics; Wireless communication
• ISSN: 2162-2337; 2162-2345
• DOI: 10.1109/LWC.2023.3240476

This letter investigates a cache-enabled multiuser mobile edge computing (MEC) system with dynamic task arrivals, taking into account the impact of proactive cache placement on the system's overall energy consumption. We consider that an access point (AP) schedules a wireless device (WD) to offload computational tasks while executing the tasks of a finite library in the task caching phase, such that the nearby WDs with the same task request arriving later can directly download the task results in the task arrival and execution phase. We aim for minimizing the system's weighted-sum energy over a finite-time horizon, by jointly optimizing the task caching decision and the MEC execution of the AP, and local computing as well as task offloading of the WDs at each time slot, subject to caching capacity, task causality, and completion deadline constraints. The formulated design problem is a mixed-integer nonlinear program. Under the assumption of fully predicable task arrivals, we first propose a branch-and-bound (BnB) based method to obtain the optimal offline solution. Next, we propose two low-complexity schemes based on convex relaxation and task-popularity, respectively. Finally, numerical results show the benefit of the proposed schemes over existing benchmark schemes.