MOFCO: Mobility- and Migration-Aware Task Offloading in Three-Layer Fog Computing Environments

TL;DR

This paper introduces MOFCO, a novel algorithm for task offloading in three-layer fog computing that accounts for user mobility, aiming to reduce system costs related to latency and energy consumption.

Contribution

It formulates the offloading problem as a MINLP and employs a heuristic-aided evolutionary game theory approach for efficient solutions in mobile fog environments.

Findings

MOFCO reduces system cost by an average of 19%.

It achieves up to 43% cost reduction in certain scenarios.

The approach effectively handles realistic mobility patterns.

Abstract

Task offloading in three-layer fog computing environments presents a critical challenge due to user equipment (UE) mobility, which frequently triggers costly service migrations and degrades overall system performance. This paper addresses this problem by proposing MOFCO, a novel Mobility- and Migration-aware Task Offloading algorithm for Fog Computing environments. The proposed method formulates task offloading and resource allocation as a Mixed-Integer Nonlinear Programming (MINLP) problem and employs a heuristic-aided evolutionary game theory approach to solve it efficiently. To evaluate MOFCO, we simulate mobile users using SUMO, providing realistic mobility patterns. Experimental results show that MOFCO reduces system cost, defined as a combination of latency and energy consumption, by an average of 19% and up to 43% in certain scenarios compared to state-of-the-art methods.