一句话：提出无服务器函数压缩与成本感知预热位置优化，以缓解冷启动并降低高内存压力下的保活开销。
无服务器计算存在严重的函数冷启动问题。现有技术通过预测函数调用时间来预热函数，以减少冷启动，但被预热的函数会占用内存并产生保活成本，在突发负载下这种成本可能变得难以承受。为此，作者设计了 CodeCrunch，提出无服务器函数压缩的概念，并利用服务器异构性来优化预热位置，从而在高内存压力下提升无服务器计算的效率。该工作的核心是在减少冷启动与降低保活/内存开销之间进行成本感知的权衡，使函数预热和放置更加高效。摘要未报告具体性能提升数值。
Serverless computing has a critical problem of function cold starts. To minimize cold starts, state-of-the-art techniques predict function invocation times to warm them up. Warmed-up functions occupy space in memory and incur a keep-alive cost, which can become exceedingly prohibitive under bursty load. To address this issue, we design CodeCrunch, which introduces the concept of serverless function compression and exploits server heterogeneity to make serverless computing more efficient, especially under high memory pressure.
@inproceedings{RoyP0T24,
title = {CodeCrunch: Improving Serverless Performance via Function Compression and Cost-Aware Warmup Location Optimization},
author = {Rohan Basu Roy and Tirthak Patel and Rohan Garg and Devesh Tiwari},
booktitle = {ASPLOS},
year = {2024},
doi = {10.1145/3617232.3624866}
}