Park: An Open Platform for Learning-Augmented Computer Systems
Authors: Hongzi Mao, Parimarjan Negi, Akshay Narayan, Hanrui Wang, Jiacheng Yang, Haonan Wang, Ryan Marcus, ravichandra addanki, Mehrdad Khani Shirkoohi, Songtao He, Vikram Nathan, Frank Cangialosi, Shaileshh Venkatakrishnan, Wei-Hung Weng, Song Han, Tim Kraska, Dr.Mohammad Alizadeh
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we present the performance of existing RL approaches over those 12 problems and outline potential areas of future work. We benchmark the 12 systems in Park with both RL methods and existing heuristic baselines ( 5). The experiments benchmark the training efficiency and the eventual performance of RL approaches on each task. |
| Researcher Affiliation | Academia | Hongzi Mao, Parimarjan Negi, Akshay Narayan, Hanrui Wang, Jiacheng Yang, Haonan Wang, Ryan Marcus, Ravichandra Addanki, Mehrdad Khani, Songtao He, Vikram Nathan, Frank Cangialosi, Shaileshh Bojja Venkatakrishnan, Wei-Hung Weng, Song Han, Tim Kraska, Mohammad Alizadeh MIT Computer Science and Artificial Intelligence Laboratory park-project@csail.mit.edu |
| Pseudocode | Yes | In Figure 3, Algorithm 1 depicts this interaction process. ... In Figure 3, Algorithm 2 outlines this option in simulated system environments. |
| Open Source Code | Yes | We open-source Park as well as the RL agents and baselines in https://github.com/park-project/park. |
| Open Datasets | Yes | For example, for the CDN memory caching environment, we use an open dataset containing 500 million requests, collected from a public CDN serving top-ten US websites [15]. and CIFAR-10 [52] and Penn Tree Bank [65]. |
| Dataset Splits | No | The paper mentions training and testing but does not provide specific details on how the dataset was split into training, validation, and test sets (e.g., percentages or sample counts). |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., CPU, GPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper mentions various software and frameworks like DQN, A2C, Policy Gradient, DDPG, TensorFlow, Open AI Gym, and Open AI Baselines, but it does not specify their version numbers. |
| Experiment Setup | Yes | The details of hyperparameter tunings, agent architecture and system configurations are in Appendix B. |