A Parallel Optimal Task Allocation Mechanism for Large-Scale Mobile Edge Computing

TL;DR

This paper introduces a parallel optimization mechanism for large-scale mobile edge computing that reduces delay and energy consumption through a novel task allocation model and efficient solution algorithms.

Contribution

It proposes a parallel multi-block ADMM-based method for joint task optimization in MEC, addressing delay and energy efficiency with guaranteed convergence and scalability.

Findings

Significant reduction in delay and energy consumption demonstrated.

Efficient algorithms ensure convergence and scalability.

Effective for large-scale MEC applications.

Abstract

We consider the problem of intelligent and efficient task allocation mechanism in large-scale mobile edge computing (MEC), which can reduce delay and energy consumption in a parallel and distributed optimization. In this paper, we study the joint optimization model to consider cooperative task management mechanism among mobile terminals (MT), macro cell base station (MBS), and multiple small cell base station (SBS) for large-scale MEC applications. We propose a parallel multi-block Alternating Direction Method of Multipliers (ADMM) based method to model both requirements of low delay and low energy consumption in the MEC system which formulates the task allocation under those requirements as a nonlinear 0-1 integer programming problem. To solve the optimization problem, we develop an efficient combination of conjugate gradient, Newton and linear search techniques based algorithm with Logarithmic Smoothing (for global variables updating) and the Cyclic Block coordinate Gradient Projection (CBGP, for local variables updating) methods, which can guarantee convergence and reduce computational complexity with a good scalability. Numerical results demonstrate the effectiveness of the proposed mechanism and it can effectively reduce delay and energy consumption for a large-scale MEC system.