Pronghorn: Effective Checkpoint Orchestration for Serverless Hot-Starts

Serverless computing allows developers to deploy and scale stateless functions in ephemeral workers easily. As a result, serverless computing has been widely used for many applications, such as computer vision, video processing, and HTML generation. However, we find that the stateless nature of serverless computing wastes many of the important benefits modern language runtimes have to offer. A notable example is the extensive profiling and Just-in-Time (JIT) compilation effort that runtimes implement to achieve acceptable performance of popular high-level languages, such as Java, JavaScript, and Python. Unfortunately, when modern language runtimes are naively adopted in serverless computing, all of these efforts are lost upon worker eviction. Checkpoint-restore methods alleviate the problem by resuming workers from snapshots taken after initialization. However, production-grade language runtimes can take up to thousands of invocations to fully optimize a single function, thus rendering naive checkpoint-restore policies ineffective.

This paper proposes Pronghorn, a snapshot serverless orchestrator that automatically monitors the function performance and decides (1) when it is the right moment to take a snapshot and (2) which snapshot to use for new workers. Pronghorn is agnostic to the underlying platform and JIT runtime, thus easing its integration into existing runtimes and worker deployment environments (container, virtual machine, etc.). On a set of representative serverless benchmarks, Pronghorn provides end-to-end median latency improvements of 37.2% across 9 out of 13 benchmarks (20-58% latency reduction) when compared to state-of-art checkpointing policies.