一句话：提出集中式缓存控制器 Flame，以解决 Serverless 计算中跨服务器负载偏斜导致的缓存效率低下问题。
缓存函数是缓解 Serverless 计算冷启动开销的有前景手段，但缓存会显著增加资源成本，因此如何做出缓存决策仍具挑战。作者观察到，由于不同服务器之间的工作负载存在偏斜，先前的“本地缓存控制”设计难以实现高缓存效率。为此，论文提出 Flame——一种集中式缓存控制器，旨在从全局视角协调缓存决策，以改善缓存效率并兼顾资源成本。摘要未给出具体实现机制与实验数据，但明确指出集中控制是应对服务器间负载偏斜的关键方向。
Caching function is a promising way to mitigate coldstart overhead in serverless computing. However, as caching also increases the resource cost significantly, how to make caching decisions is still challenging. We find that the prior "local cache control" designs are insufficient to achieve high cache efficiency due to the workload skewness across servers.
@inproceedings{paperbot10110,
title = {Flame: A Centralized Cache Controller for Serverless Computing},
author = {Yanan Yang and Laiping Zhao and Yiming Li and Shihao Wu and Yuechan Hao and Yuchi Ma and Keqiu Li},
booktitle = {ASPLOS},
year = {2023},
doi = {10.1145/3623278.3624769}
}