Memory Deduplication for Serverless Computing with Medes

Abstract

Serverless platforms today impose rigid trade-offs between resource use and user-perceived performance. 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 a serverless framework, Medes, that breaks the rigid trade-off and allows operators to navigate the trade-off space smoothly. Medes leverages the fact that the warm sandboxes running on serverless platforms have a high fraction of duplication in their memory footprints. We exploit these redundant chunks 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 develop novel mechanisms to identify memory redundancy at minimal overhead while ensuring that the dedup containers' memory footprint is small. Finally, we develop a simple sandbox management policy that exposes a narrow, intuitive interface for operators to trade-off performance for memory by jointly controlling warm and dedup sandboxes. Detailed experiments with a prototype using real-world serverless workloads demonstrate that Medes can provide up to 1×-2.75× improvements in the end-to-end latencies. The benefits of Medes are enhanced in memory pressure situations, where Medes can provide up to 3.8× improvements in end-to-end latencies. Medes achieves this by reducing the number of cold starts incurred by 10-50% against the state-of-the-art baselines.