Base Station Selection and Task Offloading of the Mobile Edge Computing System

TL;DR

This paper develops a dynamic decision-making model for base station selection and task offloading in mobile edge computing, aiming to minimize latency and improve service quality amid user mobility.

Contribution

It introduces a novel iterative optimization framework that considers switching, communication, and queuing delays to enhance MEC system performance.

Findings

Effective reduction in overall latency achieved.

Optimized service deployment adapts to user mobility.

Improved quality of service demonstrated.

Abstract

Based on the two decision variables, service location and base station selection, construct a computational model of the switching delay, communication delay, and queuing delay patterns of a mobile edge computing system in each time horizon; minimize the non-switching latency to obtain the service deployment and base station selection decision at the initial time horizon; compute the switching latency and non-switching latency of the current time horizon based on the decision of the previous time horizon, and determine the current time horizon based on the principle of larger non-switching latency tolerance. If the service is not reallocated, the decision for the current time slot remains the same as the decision for the previous time slot; if the service is reallocated, the non-reach time of the current time slot is minimized and the service allocation and base station selection decision for the current time slot is obtained; the service allocation and base station selection decisions for all time slots are iteratively computed. This paper provides low-latency processing and quality of service for users to satisfy the randomness of user movement.