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首页> 外文会议>Proceedings of the 2017 IEEE International Conference on Applied System Innovation >Using data mining and machine learning techniques for system design space exploration and automatized optimization
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Using data mining and machine learning techniques for system design space exploration and automatized optimization

机译：使用数据挖掘和机器学习技术进行系统设计空间探索和自动化优化

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摘要

Recently, the significance of data mining and machine learning have been highlighted in diversified application scenarios. Various data mining and machine learning techniques are often used to analyze the gigantic amount of data to create more commercial values in high-end enterprise systems. However, the advancement of technologies has made data mining and machine learning possible on low-end systems, such as personal computers or embedded systems. While researchers have proposed excellent work on the management de-signs of different components of the system, most of the work are built upon the characteristics of the system, which may change from time to time. This makes it impossible to optimize the system performance with stat-ic, or statically adaptive, system designs. In this work, we propose to embed the supports of data mining and machine learning to the design of operating system, so as to discover a new, automatized way to adaptively optimize the system without using complex algorithms. To validate the proposed ideas, we choose the cache design as a case study, where the replacement of cached contents is automatically controlled by a decision maker. The decision maker then replies on a data miner, which analyzes the data collected by the system monitor. The efficacy of the considered case is verified by a series of experiments, where the results are quite encouraging.
机译：最近，数据挖掘和机器学习的重要性已在各种应用场景中得到了强调。通常使用各种数据挖掘和机器学习技术来分析庞大的数据量，以在高端企业系统中创造更多的商业价值。但是，技术的进步使得在低端系统（例如个人计算机或嵌入式系统）上进行数据挖掘和机器学习成为可能。尽管研究人员已就系统不同组件的管理设计提出了出色的工作，但大多数工作都是建立在系统特性之上的，该特性可能会不时发生变化。这使得无法通过静态或静态自适应系统设计来优化系统性能。在这项工作中，我们建议将数据挖掘和机器学习的支持嵌入到操作系统的设计中，以便发现一种新的，自动化的方法，以在不使用复杂算法的情况下自适应地优化系统。为了验证所提出的想法，我们选择了缓存设计作为案例研究，由决策者自动控制缓存内容的替换。然后，决策者回复数据挖掘器，该数据挖掘器分析系统监视器收集的数据。通过一系列实验验证了所考虑案例的有效性，结果令人鼓舞。

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