Dynamic Pre-Warm Strategies for Reducing Cold-Start Latency in Serverless Edge Computing

Serverless edge computing uses lightweight containers to execute IoT applications on-demand, enhancing resource utilization efficiency at the network edge. However, cold-start latency, occurring when a container is instantiated from scratch, degrades IoT service responsiveness. Effective mitigation strategies are needed to maintain service quality. Current container caching strategies often fail to account for diverse workloads and predictable request patterns. This paper introduces a dynamic container pre-warm model that adjusts container states based on predicted future requests, mitigating cold-start overhead. Our approach employs a real-time algorithm for container instantiation and termination, balancing latency reduction and resource conservation. Inte-grating predictive analytics with adaptive resource management, we evaluate our model using a trace-driven simulation framework with real-world data. Our experiments show that the proposed strategy reduces cold starts by up to 60 % and improves average response times by 45 % compared to state-of-the-art methods. Additionally, our approach adapts to varying workloads, providing a scalable and robust solution for dynamic edge environments.