As an emerging cloud computing deployment paradigm, serverless computing is gaining traction due to its efficiency and ability to harness on-demand cloud resources. However, a significant hurdle remains in the form of the cold start problem, causing latency when launching new function instances from scratch. Existing solutions tend to use over-simplistic strategies for function pre-loading/unloading without full invocation pattern exploitation, rendering unsatisfactory optimization of the trade-off between cold start latency and resource waste. To bridge this gap, we propose SPES, the first differentiated scheduler for runtime cold start mitigation by optimizing serverless function provision. Our insight is that the common architecture of serverless systems prompts the con- centration of certain invocation patterns, leading to predictable invocation behaviors. This allows us to categorize functions and pre-load/unload proper function instances with finer-grained strategies based on accurate invocation prediction. Experiments demonstrate the success of SPES in optimizing serverless function provision on both sides: reducing the 75th-percentile cold start rates by 49.77% and the wasted memory time by 56.43%, compared to the state-of-the-art. By mitigating the cold start issue, SPES is a promising advancement in facilitating cloud services deployed on serverless architectures.

翻译：作为新兴的云计算部署范式，无服务器计算因其高效性及按需利用云资源的能力而日益受到关注。然而，冷启动问题仍是重大障碍——从零开始启动新函数实例会导致延迟。现有解决方案通常采用过于简化的函数预加载/卸载策略，未能充分挖掘调用模式，导致冷启动延迟与资源浪费之间的权衡优化效果不佳。为弥补这一空白，本文提出SPES——首个通过优化无服务器函数配置来缓解运行时冷启动问题的差异化调度器。我们的核心洞察在于：无服务器系统的通用架构促使某些调用模式趋于集中，从而形成可预测的调用行为。这使得我们能够基于准确的调用预测，对函数进行分类，并采用更细粒度的策略预加载/卸载合适的函数实例。实验表明，SPES在优化无服务器函数配置方面取得了双重成效：与现有最优方案相比，其将第75百分位冷启动率降低49.77%，内存浪费时间减少56.43%。通过缓解冷启动问题，SPES为推动部署于无服务器架构上的云服务发展提供了有前景的进步。