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

2025-07-16

AI Summary

To address the frequent service migrations induced by user equipment (UE) mobility in three-tier fog computing—leading to substantial system overhead and latency—this paper proposes a mobility- and migration-aware joint task offloading framework. The method innovatively integrates evolutionary game theory with heuristic strategies to jointly optimize task scheduling, resource allocation, and migration decisions under dynamic network conditions. A mixed-integer nonlinear programming (MINLP) model is formulated using realistic UE mobility traces generated by SUMO, and an efficient solution mechanism is designed. Experimental results demonstrate that, compared to state-of-the-art approaches, the proposed framework reduces the weighted sum of latency and energy consumption—the system’s综合 cost—by up to 43% and by 19% on average. This significantly enhances long-term performance balance and resource utilization efficiency.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Evolutionary Computation
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization

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.

Problem

• Addresses task offloading challenges in fog computing due to UE mobility
• Proposes MOFCO to minimize costly service migrations and performance degradation
• Reduces system cost by optimizing latency and energy consumption

Innovation

• MINLP problem formulation for task offloading
• Heuristic-aided evolutionary game theory approach
• SUMO-based realistic mobility simulation