The Importance of Generalizability in Machine Learning for Systems

IF 1.4 3区计算机科学 Q4 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

IEEE Computer Architecture Letters Pub Date : 2024-04-02 DOI:10.1109/LCA.2024.3384449

Varun Gohil;Sundar Dev;Gaurang Upasani;David Lo;Parthasarathy Ranganathan;Christina Delimitrou

引用次数: 0

Abstract

Using machine learning (ML) to tackle computer systems tasks is gaining popularity. One of the shortcomings of such ML-based approaches is the inability of models to generalize to out-of-distribution data i.e., data whose distribution is different than the training dataset. We showcase that this issue exists in cloud environments by analyzing various ML models used to improve resource balance in Google's fleet. We discuss the trade-offs associated with different techniques used to detect out-of-distribution data. Finally, we propose and demonstrate the efficacy of using Bayesian models to detect the model's confidence in its output when used to improve cloud server resource balance.

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系统机器学习中通用性的重要性

使用机器学习（ML）来处理计算机系统任务越来越受欢迎。这种基于 ML 的方法的缺点之一是模型无法泛化到分布外数据，即分布与训练数据集不同的数据。我们通过分析用于改善谷歌机队资源平衡的各种 ML 模型，展示了云环境中存在的这一问题。我们讨论了与用于检测分布失衡数据的不同技术相关的权衡问题。最后，我们提出并展示了使用贝叶斯模型检测模型在用于改善云服务器资源平衡时对其输出的置信度的功效。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Computer Architecture Letters COMPUTER SCIENCE, HARDWARE & ARCHITECTURE-

CiteScore

4.60

自引率

4.30%

发文量

期刊介绍： IEEE Computer Architecture Letters is a rigorously peer-reviewed forum for publishing early, high-impact results in the areas of uni- and multiprocessor computer systems, computer architecture, microarchitecture, workload characterization, performance evaluation and simulation techniques, and power-aware computing. Submissions are welcomed on any topic in computer architecture, especially but not limited to: microprocessor and multiprocessor systems, microarchitecture and ILP processors, workload characterization, performance evaluation and simulation techniques, compiler-hardware and operating system-hardware interactions, interconnect architectures, memory and cache systems, power and thermal issues at the architecture level, I/O architectures and techniques, independent validation of previously published results, analysis of unsuccessful techniques, domain-specific processor architectures (e.g., embedded, graphics, network, etc.), real-time and high-availability architectures, reconfigurable systems.