Mitigating cold start problem in serverless computing using predictive pre-warming with machine learning

The cold start problem in serverless computing leads to increased latency when functions are invoked after being idle. This paper proposes a predictive pre-warming strategy that leverages machine learning and historical data analysis to mitigate the cold start problem. By using a Recurrent Neural Network (RNN) to predict future invocations and a pre-warming scheduler to determine the number of instances to pre-warm, our approach aims to optimize resource utilization and reduce latency. Results show that the proposed method adapts idle-container times efficiently, reducing cold starts and idle periods. The proposed method outperforms OpenWhisk by executing more invocations, demonstrating a 49.52% improvement and enhancing container resource allocation optimization.