Paper Info
Title
Park: An Open Platform for Learning-Augmented Computer Systems
Abstract
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.
Year
Venue
DocType
2019
ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 32 (NIPS 2019)
Conference
Volume
ISSN
Citations 
32
1049-5258
0
PageRank 
References 
Authors
0.34
0
17
Name
Order
Citations
PageRank
Hongzi Mao145118.89
Parimarjan Negi2182.91
Akshay Narayan300.34
Hanrui Wang400.34
Jiacheng Yang500.34
Haonan Wang600.34
Ryan Marcus76411.51
Ravichandra Addanki831.38
Mehrdad Khani921.71
Songtao He1081.37
Vikram Nathan1172.11
Frank Cangialosi1200.34
Shaileshh Bojja Venkatakrishnan13435.81
Wei-Hung Weng1440.76
Song Han1502.37
Tim Kraska1600.34
Mohammad Alizadeh1700.34