Dynamic service prioritization with predicted intervals for QoS-sensitive service migrations in MEC - Velrajan, Saravanan; Sharmila, V. Ceronmani (Service oriented computing and applications, 2025)
Service migrations in Multi-access Edge Compute (MEC) infrastructure are complex due to the geographically distributed infrastructure, limited availability of resources at the edge and heterogenous applications hosted in the infrastructure. Several existing studies have investigated service migrations in MEC to handle user mobility and energy savings. However, the intricate challenge of migrating thousands of user sessions in real-time during application overload conditions without impacting the users’ QoS has received limited attention. Ad-hoc service migrations in MEC pose a significant risk of service disruptions, affecting users' SLAs and degrading service performance for latency-sensitive applications. We propose a novel Time-aware Closed-loop Adaptive Particle Swarm Optimization (T-CLAPSO) algorithm that prioritises services to migrate in real-time based on application load and predicted service migration interval, minimising the impact on users’ QoS. Unlike existing studies that rely solely on application load-based heuristics, T-CLAPSO uniquely models the application performance and service migration time constraints as a combinatorial optimisation problem to prioritise service migrations while maintaining users’ QoS. T-CLAPSO algorithm reduces SLA violations during service migrations by 22% compared to the state-of-the-art Exponentially Weighted Moving Average with three-parameter smoothing (EWMA3) and 38% compared to the standard PSO algorithms, improving the user experience for QoS-sensitive applications such as video streaming, IoT Smart meter and Surveillance.