"Learned"

Q3 Computer Science Operating Systems Review (ACM) Pub Date : 2019-07-25 DOI:10.1145/3352020.3352027
Yiying Zhang, Yutong Huang
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引用次数: 14

Abstract

With operating systems being at the core of computer systems, decades of research and engineering efforts have been put into the development of OSes. To keep pace with the speed of modern hardware and application evolvement, we argue that a different approach should be taken in future OS development. Instead of relying solely on human wisdom, we should also leverage AI and machine learning techniques to automatically "learn" how to build and tune an OS. This paper explores the opportunities and challenges of the "learned" OS approach and makes recommendation for future researchers and practitioners on building such an OS.
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“学习”
由于操作系统是计算机系统的核心,数十年的研究和工程努力已经投入到操作系统的开发中。为了跟上现代硬件和应用程序发展的速度,我们认为在未来的操作系统开发中应该采取不同的方法。我们不应该仅仅依靠人类的智慧,还应该利用人工智能和机器学习技术来自动“学习”如何构建和调整操作系统。本文探讨了“学习型”操作系统方法的机遇和挑战,并为未来的研究人员和实践者构建这样的操作系统提出了建议。
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
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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