Generalizable Resource Allocation in Stream Processing via Deep Reinforcement Learning
Authors: Xiang Ni, Jing Li, Mo Yu, Wang Zhou, Kun-Lung Wu857-864
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments show that the proposed model outperforms both METIS, a state-of-the-art graph partitioning algorithm, and an LSTM-based encoder-decoder model, in about 70% of the test cases. |
| Researcher Affiliation | Collaboration | Citadel, IBM Research, New Jersey Institute of Technology |
| Pseudocode | No | The paper describes the model architecture and training process with equations and textual descriptions but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code and data set are released at https://github.com/ xiangni/DREAM. |
| Open Datasets | Yes | We create a new benchmark with 3,150 graphs in the data set. ... Our code and data set are released at https://github.com/ xiangni/DREAM. |
| Dataset Splits | No | The paper states 'We randomly select 2,520 graphs for training and the remaining 630 graphs for testing' but does not specify a separate validation split or how it was handled. |
| Hardware Specification | No | The paper describes the simulated environment ('We create a cluster in CEPSim with 5 homogeneous devices. The computing capacity of each device is 2.5E3 million instructions per second (MIPS). The link bandwidth between devices is 1000 Mbps.') but does not provide specific hardware details (e.g., GPU/CPU models, memory) used for training the deep reinforcement learning model itself. |
| Software Dependencies | No | The paper mentions using an 'LSTM' and 'Adam optimizer' for training, and that it 'extend[s] CEPSim' as a simulator, but it does not provide specific version numbers for any of these software components, libraries, or the CEPSim itself. |
| Experiment Setup | Yes | The number of hops K in graph embedding is 2, and the length of node embeddings is 512. The network is trained for 40 epochs using Adam optimizer with learning rate 0.001. At each training step, only one graph is fed to the network. The number of samples N for a training graph varies from 3 to 6 (with 3 on-policy samples and up to 3 samples from memory buffer). These settings are selected via cross-validation. |