Correlation-Based Device Energy-Efficient Dynamic Multi-Task Offloading for Mobile Edge Computing

TL;DR

This paper proposes a correlation-based dynamic multi-task offloading strategy for mobile edge computing that reduces device energy consumption by optimizing task transmission and device parameters.

Contribution

It introduces a novel offloading approach leveraging task correlations and a binary decision tree algorithm for joint optimization of device parameters.

Findings

Reduces device energy consumption compared to conventional methods.

Effectively optimizes task offloading considering task correlations.

Demonstrates improved performance through MATLAB simulations.

Abstract

Task offloading to mobile edge computing (MEC) has emerged as a key technology to alleviate the computation workloads of mobile devices and decrease service latency for the computation-intensive applications. Device battery consumption is one of the limiting factors needs to be considered during task offloading. In this paper, multi-task offloading strategies have been investigated to improve device energy efficiency. Correlations among tasks in time domain as well as task domain are proposed to be employed to reduce the number of tasks to be transmitted to MEC. Furthermore, a binary decision tree based algorithm is investigated to jointly optimize the mobile device clock frequency, transmission power, structure and number of tasks to be transmitted. MATLAB based simulation is employed to demonstrate the performance of our proposed algorithm. It is observed that the proposed dynamic multi-task offloading strategies can reduce the total energy consumption at device along various transmit power versus noise power point compared with the conventional one.