The Hybrid Model: Prediction-Based Scheduling and Efficient Resource Management in a Serverless Environment

Serverless computing has gained significant attention in recent years. However, the cold start problem remains a major challenge, not only because of the substantial latency it introduces to function execution time, but also because frequent cold starts lead to poor resource utilization, especially during workload fluctuations. To address these issues, we propose a multi-level scheduling solution: the Hybrid Model. This model is designed to reduce the frequency of cold starts while maximizing container utilization. At the global level (across invokers), the Hybrid Model employs a skewness-aware scheduling strategy to select the most appropriate invoker for each request. Within each invoker, we introduce a greedy buffer-aware scheduling method that leverages the available slack (remaining buffer) of warm containers to aggressively encourage their reuse. Both the global and the local schedule are tightly integrated with a prediction component- The Hybrid Predictor- that combines Auto-Regressive Integrated Moving Average ARIMA (linear trends) and Random Forest (non-linear residuals + environment-aware features) for 5-min workload forecasts. The Hybrid Model is implemented on Apache OpenWhisk and evaluated using Azure-like traces and real FaaS applications. The evaluations show that the Hybrid Model achieves up to 34% SLA violation reductions compared to three state-of-the-art approaches and maintains the container utilization to be more than 80%.