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Task completion-oriented service migration for connected autonomous vehicles in multi-server edge computing
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AI
DOI：10.1016/j.comnet.2026.112533.png)
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Abstract
En 中文
To address the urgent practical challenge of service migration for connected autonomous vehicles (CAVs) in mobile edge computing (MEC), this study aims to maximize the task completion rate, particularly for safety-critical operations. Existing approaches often overlook the completion status of tasks with different priority levels during frequent service migrations and fail to co-optimize multiple constraints such as energy consumption, latency, and offloading cost. Consequently, it remains difficult to reliably complete highly urgent tasks in resource-constrained edge environments. To tackle this issue, we propose a comprehensive two-stage solution: the Improved Task Offloading and Service Migration (ITOSM) algorithm. In the first stage, a weighted evaluation model based on information entropy is constructed by integrating transmission time, execution time, and offloading cost. Tasks are offloaded to the edge server with either the highest or second-highest weighted sum according to their urgency level. In the second stage, service migration decisions are optimized using an improved binary particle swarm optimization (BPSO) algorithm with enhanced local search capability. Experimental results demonstrate that ITOSM outperforms existing methods, achieving up to 10.00% higher completion rates for Extremely Important Tasks (EITs) with strict deadlines. This improvement directly contributes to safer and more reliable CAV operations, highlighting the practical significance of this work for intelligent transportation systems.
Keywords:
Connected autonomous vehicle
Task deadlines
Mobile edge computing
Service migration
User mobility
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