Park: An Open Platform for Learning-Augmented Computer Systems

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.

Authors

• Hongzi Mao
• Parimarjan Negi MIT CSAIL
• Akshay Narayan
• Hanrui Wang Massachusetts Institute of Technology
• Jiacheng Yang
• Haonan Wang
• Ryan Marcus MIT CSAIL
• ravichandra addanki Massachusetts Institute of Technology
• Mehrdad Khani Shirkoohi MIT
• Songtao He
• Vikram Nathan MIT
• Frank Cangialosi MIT CSAIL
• Shaileshh Venkatakrishnan MIT
• Wei-Hung Weng MIT
• Song Han
• Tim Kraska
• Dr. Mohammad Alizadeh Massachusetts institute of technology

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