Device vs Edge Computing for Mobile Services: Delay-aware Decision Making to Minimize Power Consumption

TL;DR

This paper explores power-efficient data offloading in mobile edge computing, balancing delay constraints and power consumption, and proposes algorithms that significantly reduce power use compared to local processing.

Contribution

It introduces a novel optimization framework with centralized and distributed algorithms for joint power and channel management in mobile edge computing.

Findings

Proposed algorithms achieve up to 60% power savings.

Data offloading can be more power-efficient than local computing under certain conditions.

Delay constraints are effectively incorporated into the power minimization problem.

Abstract

A promising technique to provide mobile applications with high computation resources is to offload the processing task to the cloud. Utilizing the abundant processing capabilities of the clouds, mobile edge computing enables mobile devices with limited batteries to run resource hungry applications and to save power. However, it is not always true that edge computing consumes less power compared to device computing. It may take more power for the mobile device to transmit a file to the cloud than running the task itself. This paper investigates the power minimization problem for the mobile devices by data offloading in multi-cell multi-user OFDMA mobile edge computing networks. We consider the maximum acceptable delay as QoS metric to be satisfied in our network. We formulate the problem as a mixed integer nonlinear problem which is converted into a convex form using D.C. approximation. To solve the converted optimization problem, we have proposed centralized and distributed algorithms for joint power allocation and channel assignment together with decision-making. Simulation results illustrate that by utilizing the proposed algorithms, considerable power savings can be achieved, e.g., about 60 % for large bit stream size compared to local computing baseline.