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<p>In the era of time-critical applications such as healthcare, Edge Computing (EC) has emerged as a pivotal paradigm for delivering low-latency, responsive services by processing data closer to end users. However, the inherent resource constraints of&#xa0;edge nodes pose the challenge of maintaining adequate Quality of Service (QoS) for end users while simultaneously minimizing service delays, preventing resource overloading, and maintaining lower energy consumption at edge nodes. This paper presents a Mobility-Aware Multi-Objective (MAMO) Decision-Making framework for reliable service provisioning in dynamic edge environments. Our approach integrates an AHP-based Edge-Rank mechanism for initial server prioritization with a modified Strength Pareto Evolutionary Algorithm II (SPEA2) enhanced by Resource-Aware Fitness Assignment (RAFA) and Adaptive Crowding Distance (ACD) to fine-tune the server selection. The framework simultaneously optimizes four critical objectives: minimizing service delay violations, reducing resource-stressed faulty services, reducing service relocations, and conserving energy consumption. Extensive evaluation using real-world taxi trajectories and clinical urgency levels demonstrates significant performance improvements. Our AHP-Guided SPEA2 achieves a 15.3% reduction in faulty services, 2.9% in energy savings, lower number of service relocations, while providing differentiated QoS across four clinical service urgency levels. The hybrid approach reduces convergence time through intelligent AHP ranking initialization, ensuring scalability for real-time deployment. These results highlight the framework’s effectiveness in delivering reliable, energy-efficient edge services for mobility-sensitive applications while navigating multi-objective trade-offs.</p>

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MAMO-Edge: A Mobility-Aware Multi-Objective Approach to Improve QoS & Faulty Services in Federated Edges

  • 期刊论文

  • 发表日期：

    2026年1月21日

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  • Majid Ayoubi,
  • Mingchu Li,
  • Mohammed Albishari,
  • Ali Al-Daoar,
  • Rahimullah Rabih

摘要

In the era of time-critical applications such as healthcare, Edge Computing (EC) has emerged as a pivotal paradigm for delivering low-latency, responsive services by processing data closer to end users. However, the inherent resource constraints of edge nodes pose the challenge of maintaining adequate Quality of Service (QoS) for end users while simultaneously minimizing service delays, preventing resource overloading, and maintaining lower energy consumption at edge nodes. This paper presents a Mobility-Aware Multi-Objective (MAMO) Decision-Making framework for reliable service provisioning in dynamic edge environments. Our approach integrates an AHP-based Edge-Rank mechanism for initial server prioritization with a modified Strength Pareto Evolutionary Algorithm II (SPEA2) enhanced by Resource-Aware Fitness Assignment (RAFA) and Adaptive Crowding Distance (ACD) to fine-tune the server selection. The framework simultaneously optimizes four critical objectives: minimizing service delay violations, reducing resource-stressed faulty services, reducing service relocations, and conserving energy consumption. Extensive evaluation using real-world taxi trajectories and clinical urgency levels demonstrates significant performance improvements. Our AHP-Guided SPEA2 achieves a 15.3% reduction in faulty services, 2.9% in energy savings, lower number of service relocations, while providing differentiated QoS across four clinical service urgency levels. The hybrid approach reduces convergence time through intelligent AHP ranking initialization, ensuring scalability for real-time deployment. These results highlight the framework’s effectiveness in delivering reliable, energy-efficient edge services for mobility-sensitive applications while navigating multi-objective trade-offs.