根据输入特征自动配置函数内存，显著降低资源消耗与成本。
Input-Based Ensemble-Learning Method for Dynamic Memory Configuration of Serverless Computing Functions
- 基于输入特征构建多输出随机森林模型，预测最优内存配置。
- 实测可减少82%资源使用，降低87%运行成本。
- 适合需要高效资源管理的Serverless开发者和云服务商。
在当前的函数即服务（FaaS）中，开发者通常需手动配置函数内存，进而决定CPU、网络等资源分配。然而，正确设定内存需推测性能并做出临时决策。近期研究表明，函数输入特征（如输入大小、类型、数量）显著影响其资源需求、运行性能及成本，尤其在动态负载下更难处理。因此，引入输入感知的内存分配机制不仅能完全隐藏资源配置复杂性、提升开发效率，还能减少资源浪费，并支持更精细的成本优化定价。为此，本文提出MemFigLess，一种输入感知的无服务器内存配置方案。该框架通过离线阶段的函数剖析收集指标，并训练多输出随机森林回归模型，实现输入感知的最优资源配置。我们在AWS Lambda上评估了该方案，结果表明，MemFigLess能有效捕捉输入-资源关系，在保持性能的同时，最多节省82%资源占用，降低87%运行成本。
原文摘要 · Abstract (English)
In today's Function-as-a-Service offerings, a programmer is usually responsible for configuring function memory for its successful execution, which allocates proportional function resources such as CPU and network. However, right-sizing the function memory force developers to speculate performance and make ad-hoc configuration decisions. Recent research has highlighted that a function's input characteristics, such as input size, type and number of inputs, significantly impact its resource demand, run-time performance and costs with fluctuating workloads. This correlation further makes memory configuration a non-trivial task. On that account, an input-aware function memory allocator not only improves developer productivity by completely hiding resource-related decisions but also drives an opportunity to reduce resource wastage and offer a finer-grained cost-optimised pricing scheme. Therefore, we present MemFigLess, a serverless solution that estimates the memory requirement of a serverless function with input-awareness. The framework executes function profiling in an offline stage and trains a multi-output Random Forest Regression model on the collected metrics to invoke input-aware optimal configurations. We evaluate our work with the state-of-the-art approaches on AWS Lambda service to find that MemFigLess is able to capture the input-aware resource relationships and allocate upto 82% less resources and save up to 87% run-time costs.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。