Task scheduling strategy for mitigating cold start impact in serverless edge computing optical networks

Abstract

As emerging technologies advance, the demand for real-time processing of large-scale data grows increasingly critical. This paper focuses on a scenario of serverless edge computing (SEC) supported by optical networks, which integrates SEC’s key features (e.g., auto-scaling and edge deployment of computing resources) with the transmission advantages of optical networks to enable efficient data processing. However, this scenario brings new challenges beyond the scope of traditional task scheduling strategies. On the one hand, task scheduling needs to consider the resource limitations of computing nodes and dependencies between serverless functions; on the other hand, cold start issues caused by the “scale-to-zero” characteristic of SEC significantly impact latency-sensitive tasks. Moreover, existing container warming strategies for mitigating cold start suffer from resource waste and are disconnected from network scheduling. Therefore, this paper proposes a container warming and task scheduling strategy based on reinforcement learning (CWS-RL), which aims to mitigate the impact of cold start, reduce task latency, and control container warming costs. It makes dynamic container warming decisions based on long short-term memory (LSTM) network prediction results and incorporates the dependency slack characteristics of serverless tasks. Meanwhile, it adopts the Deep Deterministic Policy Gradient (DDPG) algorithm to achieve collaborative optimization of container warming and communication scheduling. Compared to the four baseline algorithms, CWS-RL achieves an average latency reduction of 24.08% and an average container warming costs reduction of 17.48%.