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Improving the Serverless Function Cache Efficiency With Flame
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PRE
AI
DOI：10.1109/TC.2025.3642338.png)
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Abstract
En 中文
Function caching is one of the fundamental techniques in FaaS platforms to alleviate coldstart overhead. However, as cache instances consume significant cloud resources (e.g., memory), it is challenging to balance function performance and cache cost. Current systems use simple and rudderless cache polices with a “local cache control” design, which ignores function characteristics such as workload skewness from hot functions and results in either cache contentions or cache resource waste. In this paper, inspired by software-defined networks, we propose Flame, an efficient cache system to manage cached functions with hotspot-aware instance scheduling and cache allocation. It consists of a two-layer design. Firstly, by decoupling the cache control plane from worker nodes and introducing a centralized cache controller, Flame can schedule functions from a global view of the cluster’s status, thereby reducing inter-node workload skew. Second, Flame divides the prior monolithic cache pool within each node into multiple partitions and dynamically assigns them to different hot functions, thereby further mitigating intra-node cache contention. Experimental results from real-world workloads show that Flame can reduce cache resource usage by 36% on average while improving function performance by nearly 7$\boldsymbol{\times}$ compared to the state-of-the-art method.
Keywords:
Serverless
coldstart overhead
function caching
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