Dynamic Computation Offloading for Mobile-Edge Computing with Energy Harvesting Devices

TL;DR

This paper proposes a low-complexity, online computation offloading algorithm for energy-harvesting mobile-edge computing systems, optimizing latency and task failure without prior distribution knowledge, validated through simulations.

Contribution

It introduces the Lyapunov optimization-based dynamic offloading (LODCO) algorithm for green MEC with EH devices, enabling real-time decisions based on current information.

Findings

The LODCO algorithm achieves asymptotic optimality.

Simulation results verify the effectiveness of the proposed method.

The algorithm operates with low complexity and no prior distribution knowledge.

Abstract

Mobile-edge computing (MEC) is an emerging paradigm to meet the ever-increasing computation demands from mobile applications. By offloading the computationally intensive workloads to the MEC server, the quality of computation experience, e.g., the execution latency, could be greatly improved. Nevertheless, as the on-device battery capacities are limited, computation would be interrupted when the battery energy runs out. To provide satisfactory computation performance as well as achieving green computing, it is of significant importance to seek renewable energy sources to power mobile devices via energy harvesting (EH) technologies. In this paper, we will investigate a green MEC system with EH devices and develop an effective computation offloading strategy. The execution cost, which addresses both the execution latency and task failure, is adopted as the performance metric. A low-complexity online algorithm, namely, the Lyapunov optimization-based dynamic computation offloading (LODCO) algorithm is proposed, which jointly decides the offloading decision, the CPU-cycle frequencies for mobile execution, and the transmit power for computation offloading. A unique advantage of this algorithm is that the decisions depend only on the instantaneous side information without requiring distribution information of the computation task request, the wireless channel, and EH processes. The implementation of the algorithm only requires to solve a deterministic problem in each time slot, for which the optimal solution can be obtained either in closed form or by bisection search. Moreover, the proposed algorithm is shown to be asymptotically optimal via rigorous analysis. Sample simulation results shall be presented to verify the theoretical analysis as well as validate the effectiveness of the proposed algorithm.