Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
DeepWeave: Accelerating Job Completion Time with Deep Reinforcement Learning-based Coflow Scheduling
Authors: Penghao Sun, Zehua Guo, Junchao Wang, Junfei Li, Julong Lan, Yuxiang Hu
IJCAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The proposed scheme is evaluated with a simulator using real-life traces. Simulation results show that Deep Weave completes jobs at least 1.7 faster than the state-of-the-art solutions. |
| Researcher Affiliation | Collaboration | Penghao Sun1 , Zehua Guo2 , Junchao Wang1 , Junfei Li1 , Julong Lan1 and Yuxiang Hu1 1National Digital Switching System Engineering & Technological R&D Center 2Beijing Institute of Technology EMAIL, EMAIL, EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 Training process of Deep Weave |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described in this paper. |
| Open Datasets | Yes | In this paper, we use TPC-DS [tpc, 2019] to evaluate the performance of different schemes in job DAGs. |
| Dataset Splits | No | The paper mentions training the DRL agent, but it does not provide specific details on dataset splits (e.g., percentages or counts for training, validation, and testing) for its own model. |
| Hardware Specification | Yes | The simulation runs on a desktop computer equipped with an Intel i7700 CPU, GTX 1080Ti Graphics card, and 32G DDR4 RAM. |
| Software Dependencies | Yes | The DRL algorithm is implemented on Tensor Flow based on Python 3.6. |
| Experiment Setup | Yes | In our training, the learning rate α is set to 1 10 3, and the gradient descent is used for neural network parameter update with Adam optimizer [Kingma and Ba, 2014]. |