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Rethinking Deployment for Serverless Functions: A Performance-First Perspective

DescriptionServerless computing commonly adopts strong isolation mechanisms for deploying functions, which may bring significant performance overhead because each function needs to run in a completely new environment, i.e., the “one-to-one” model. To accelerate the function computation, prior work has proposed using sandbox sharing to reduce the overhead, i.e., the “many-to-one” model. Nonetheless, either process-based true-parallelism or thread-based pseudo-parallelism prevents its adaptation for latency-sensitive web services.

To achieve optimal performance and resource efficiency for serverless workflow, we argue an “m-to-n” deployment model that manipulates multiple granularities of computing abstractions (e.g., processes, threads), and sandboxes to amortize overhead. We propose wrap, a new deployment abstraction that balances the tradeoffs between interaction overhead, startup overhead and function execution. We further design Chiron, a wrap-based deployment manager that can automatically perform the orchestration of multiple computing abstractions based on performance prioritization. Our comprehensive evaluation indicates Chiron outperforms state-of-the-art systems by 1.3x-21.8x on system throughput.

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