Navigating Performance-Efficiency Tradeoffs in Serverless Computing: Deduplication to the Rescue!

Navigating the performance and efficiency trade-offs is critical for serverless platforms, where the providers ideally want to give the illusion of warm function startups while maintaining low resource costs. Limited controls, provided via toggling sandboxes between warm and cold states and keep-alives, force operators to sacrifice significant resources to achieve good performance. We present Medes, a serverless framework, that allows operators to navigate the trade-off space smoothly. Our approach takes advantage of the high duplication in warm sandboxes on serverless platforms to develop a new sandbox state, called a 'dedup state', that is more memory-efficient than the warm state and faster to restore from than the cold state. We use innovative techniques to identify redundancy with minimal overhead, and provide a simple management policy to balance performance and memory. Our evaluation demonstrates that Medes can provide up to 3.8x better end-to-end latencies and reduce the number of cold starts by 10-50% against the state-of-the-art baselines.