一句话：提出基于多智能体强化学习的混合资源配置方法 FaaSConf，联合调优硬件资源与并发度，在保证端到端 QoS 的同时降低资源成本。
无服务器计算（FaaS）中，由多个短生命周期函数组成的工作流已成为重要模式，但其资源配置比单个独立函数困难得多。一方面，工作流面临动态且并发的用户负载，QoS 违约风险升高；另一方面，工作流内某个函数的性能会受到其他函数资源重配置的影响；而在单实例并发处理模式下，并发限制又是决定每实例请求容量的关键配置参数。为此，本文提出 FaaSConf，一种 QoS 感知的混合资源配置方法，利用多智能体强化学习（MARL）同时配置硬件资源与并发度，从而在最小化资源成本的同时保证端到端 QoS。为提升决策质量，作者在 MARL 中引入注意力机制以刻画函数之间复杂的性能依赖关系，并提出安全探索策略以缓解 QoS 违约，使配置探索更安全高效。实验结果表明，FaaSConf 显著优于现有最先进方法，平均降低 26.5% 的成本，并对动态负载变化表现出鲁棒性。
Serverless computing, also known as Function-as-a-Service (FaaS), is a significant development trend in modern software system architecture. The workflow composition of multiple short-lived functions has emerged as a prominent pattern in FaaS, exposing a considerable resources configuration challenge compared to individual independent serverless functions. This challenge unfolds in two ways. Firstly, workflows frequently encounter dynamic and concurrent user workloads, increasing the risk of QoS violations. Secondly, the performance of a function can be affected by the resource reprovision of other functions within the workflow.With the popularity of the mode of concurrent processing in one single instance, concurrency limit as a critical configuration parameter imposes restrictions on the capacity of requests per instance. In this study, we present FaaSConf, a QoS-aware hybrid resource configuration approach that uses multi-agent reinforcement learning (MARL) to configure hybrid resources, including hardware resources and concurrency, thereby ensuring end-to-end QoS while minimizing resource costs. To enhance decision-making, we employ an attention technique in MARL to capture the complex performance dependencies between functions. We further propose a safe exploration strategy to mitigate QoS violations, resulting in a safer and efficient configuration exploration. The experimental results demonstrate that FaaSConf outperforms state-of-the-art approaches significantly. On average, it achieves a 26.5% cost reduction while exhibiting robustness to dynamic load changes.CCS CONCEPTS• Computing methodologies → Distributed computing methodologies.
@inproceedings{Wang0D0YHH24,
title = {FaaSConf: QoS-aware Hybrid Resources Configuration for Serverless Workflows},
author = {Yilun Wang and Pengfei Chen and Hui Dou and Yiwen Zhang and Guangba Yu and Zilong He and Haiyu Huang},
booktitle = {ASE},
year = {2024},
doi = {10.1145/3691620.3695477}
}