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

TL;DR

This paper proposes joint task offloading and cache placement strategies in energy-efficient mobile edge computing systems with dynamic tasks, optimizing energy consumption through both offline and low-complexity online methods.

Contribution

It introduces a novel joint optimization framework for cache placement and task offloading in MEC systems considering dynamic tasks and energy efficiency, with both optimal and practical schemes.

Findings

Proposed schemes outperform benchmarks in energy savings.

Joint optimization reduces system energy consumption.

Low-complexity methods are effective for real-time implementation.

Abstract

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 \emph{task caching} phase, such that the nearby WDs with the same task request arriving later can directly download the task results in the \emph{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.