Energy-Efficient Scheduling with Predictions
Authors: Eric Balkanski, Noemie Perivier, Clifford Stein, Hao-Ting Wei
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we empirically demonstrate that this framework achieves an improved performance on real and synthetic datasets. |
| Researcher Affiliation | Academia | Eric Balkanski Columbia University eb3224@columbia.edu Noemie Perivier Columbia University np2708@columbia.edu Clifford Stein Columbia University cliff@ieor.columbia.edu Hao-Ting Wei Columbia University hw2738@columbia.edu |
| Pseudocode | Yes | Algorithm 1 Two-Phase Energy Efficient Scheduling (TPE) |
| Open Source Code | No | The paper does not provide any explicit statements about the release of source code or links to code repositories for the described methodology. |
| Open Datasets | Yes | We also evaluate the two algorithms on the College Message dataset from the SNAP database [26], where the scheduler must process messages that arrive over 9 days, each with between 300 and 500 messages. |
| Dataset Splits | No | The paper describes how synthetic data and predictions for real data are generated to evaluate the algorithm under different error parameters, but it does not specify train/validation/test dataset splits for model training, as the algorithm is not a machine learning model that undergoes a training phase with such splits. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU models, CPU types, memory) used to run the experiments. |
| Software Dependencies | No | The paper does not specify any software names with version numbers that are critical for reproducing the experiments (e.g., programming languages, libraries, frameworks, or solvers with their specific versions). |
| Experiment Setup | Yes | Specifically, we consider the energy plus flow time minimization problem where F(S, J ) = Pj J cj rj and consider unit-work jobs (i.e., pj = 1 for all j) and fix α = 3. ... TPE-S is Algorithm 2 with the default setting λ = 0.02, ηshift = 1 and σ = 0.4, where σ is a parameter that controls the level of prediction error, that we call the error parameter. ... In all experiments, we use the values a = 100, M = 500. |