The Case for Learning-and-System Co-design

Q3 Computer Science
C. Liang, Hui Xue, Mao Yang, Lidong Zhou
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引用次数: 3

Abstract

While decision-makings in systems are commonly solved with explicit rules and heuristics, machine learning (ML) and deep learning (DL) have been driving a paradigm shift in modern system design. Based on our decade of experience in operationalizing a large production cloud system, Web Search, learning fills the gap in comprehending and taming the system design and operation complexity. However, rather than just improving specific ML/DL algorithms or system features, we posit that the key to unlocking the full potential of learning-augmented systems is a principled methodology promoting learning-and-system co-design. On this basis, we present the AutoSys, a common framework for the development of learning-augmented systems.
学习与系统协同设计的案例
虽然系统中的决策通常用显式规则和启发式方法来解决,但机器学习(ML)和深度学习(DL)一直在推动现代系统设计的范式转变。基于我们十年来操作大型生产云系统Web Search的经验,学习填补了理解和驯服系统设计和操作复杂性的空白。然而,我们认为,释放学习增强系统全部潜力的关键是一种促进学习和系统协同设计的原则性方法,而不仅仅是改进特定的ML/DL算法或系统特征。在此基础上,我们提出了AutoSys,这是一个用于开发学习增强系统的通用框架。
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来源期刊
Operating Systems Review (ACM)
Operating Systems Review (ACM) Computer Science-Computer Networks and Communications
CiteScore
2.80
自引率
0.00%
发文量
10
期刊介绍: Operating Systems Review (OSR) is a publication of the ACM Special Interest Group on Operating Systems (SIGOPS), whose scope of interest includes: computer operating systems and architecture for multiprogramming, multiprocessing, and time sharing; resource management; evaluation and simulation; reliability, integrity, and security of data; communications among computing processors; and computer system modeling and analysis.
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