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 | Conference PDF | Archive PDF | Plain Text | 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 sphshine@126.com, guolizihao@hotmail.com, wangjunchao11@126.com, lijunfei90@qq.com, {ndscljl, huyx}@126.com |
| 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]. |