SlackAuto: Slack-Aware Vertical Autoscaling of CPU Resources for Serverless Computing

In modern serverless platforms, vertical auto scaling becomes increasingly important as a single service instance handles multiple requests concurrently. Vertical autoscaling typically relies on a prediction mechanism to proactively adjust resource allocation based on estimated future resource demands. However, inaccurate predictions result in a waste of resources or poor performance. To address this issue, we introduce slack-awareness into existing prediction/autoscaling mechanisms. Our proposed mechanism, named SlackAuto, models resource slack as a queue and controls its length to improve both CPU efficiency and application-level performance. We utilize the drift-plus-penalty algorithm of the Lyapunov optimization technique, which does not add significant run-time overheads and provides an efficient way to integrate slack-awareness with existing mechanisms. Our implementation operates in both periodic and event-driven modes, enabling an immediate response to changes in demand. Performance measurement results show that SlackAuto outper-forms existing vertical autoscaling algorithms in terms of resource efficiency and response latency.