一句话：提出 SOL 框架，使云平台节点代理能安全地使用基于机器学习的决策，并在多种故障条件下保持鲁棒。
云平台在每个服务器节点上运行大量软件代理，负责节点运维并频繁收集数据、做出决策。然而，这些代理通常依赖预定义的静态启发式或离线分析，未能利用节点上的机器学习。本文首先刻画 Azure 中节点代理的谱系，识别最可能受益于 on-node ML 的代理类别；进而提出 SOL，一个可扩展框架，用于设计安全且能应对生产环境各类故障的 ML 代理。SOL 向代理开发者提供简单 API，并管理其编写的代理特定函数的调度与运行。作者实现三个分别管理 CPU 核、节点功耗和内存放置的 ML 代理，实验表明 ML 显著提升代理性能，且 SOL 能确保代理在多种故障条件下安全运行。
Cloud platforms run many software agents on each server node. These agents manage all aspects of node operation, and in some cases frequently collect data and make decisions. Unfortunately, their behavior is typically based on pre-defined static heuristics or offline analysis; they do not leverage on-node machine learning (ML). In this paper, we first characterize the spectrum of node agents in Azure, and identify the classes of agents that are most likely to benefit from on-node ML. We then propose SOL, an extensible framework for designing ML-based agents that are safe and robust to the range of failure conditions that occur in production. SOL provides a simple API to agent developers and manages the scheduling and running of the agent-specific functions they write. We illustrate the use of SOL by implementing three ML-based agents that manage CPU cores, node power, and memory placement. Our experiments show that (1) ML substantially improves our agents, and (2) SOL ensures that agents operate safely under a variety of failure conditions. We conclude that ML-based agents show significant potential and that SOL can help build them.
DOI 原文 · arXiv · PDF（开放获取） · DBLP
@inproceedings{abs-2201-10477,
title = {SOL: safe on-node learning in cloud platforms},
author = {Yawen Wang and Daniel Crankshaw and Neeraja J. Yadwadkar and Daniel Berger and Christos Kozyrakis and Ricardo Bianchini},
booktitle = {ASPLOS},
year = {2022},
doi = {10.1145/3503222.3507704}
}