Park: An Open Platform for Learning-Augmented Computer Systems
Park: An Open Platform for Learning-Augmented Computer Systems
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发表时间：
2019-05
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通讯作者：
Hongzi Mao;Parimarjan Negi;Akshay Narayan;Hanrui Wang;Jiacheng Yang;Haonan Wang;Ryan Marcus;
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作者：
Hongzi Mao;Parimarjan Negi;Akshay Narayan;Hanrui Wang;Jiacheng Yang;Haonan Wang;Ryan Marcus;
我们介绍Park，这是一个研究人员在计算机系统中进行强化学习（RL）实验的平台。使用RL来提高系统的性能有很大的潜力，但在许多方面也与使用RL进行游戏有很大的不同。因此，在这项工作中，我们首先讨论了系统RL所面临的独特挑战，然后提出Park一个开放的可扩展平台，这使得ML研究人员更容易解决系统问题。目前，公园由12个真实的世界系统为中心的优化问题，一个共同的易于使用的界面。最后，我们提出了现有的强化学习方法在这12个问题的性能，并概述了未来工作的潜在领域。
We present Park, a platform for researchers to experiment with Reinforcement Learning (RL) for computer systems. Using RL for improving the performance of systems has a lot of potential, but is also in many ways very different from, for example, using RL for games. Thus, in this work we first discuss the unique challenges RL for systems has, and then propose Park an open extensible platform, which makes it easier for ML researchers to work on systems problems. Currently, Park consists of 12 real world system-centric optimization problems with one common easy to use interface. Finally, we present the performance of existing RL approaches over those 12 problems and outline potential areas of future work.