Scheduling in Visual Fog Computing: NP-Completeness and Practical Efficient Solutions
Authors: Hong-Min Chu, Shao-Wen Yang, Padmanabhan Pillai, Yen-Kuang Chen
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 6 Experimental Results This section evaluates our solutions for feasibility and scalability, where the former is through experiments against realistic visual fog settings and the latter is through generalized visual fog settings. Both of them are with carefully crafted simulated data so that there only exist non-trivial solutions. All experiments were run on Intel Xeon CPU E5-4657L v2 @ 2.4GHz using MATLAB s Optimization Toolbox. |
| Researcher Affiliation | Collaboration | Hong-Min Chu National Taiwan University r04922031@csie.ntu.edu.tw Shao-Wen Yang Intel Corporation shao-wen.yang@intel.com Padmanabhan Pillai Intel Corporation padmanabhan.s.pillai@intel.com Yen-Kuang Chen Intel Corporation yen-kuang.intel.com |
| Pseudocode | No | The paper provides mathematical formulations for ILP-T and ILP-D but does not include any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code for the described methodology, nor does it include links to a code repository. |
| Open Datasets | No | The paper states, 'Both of them are with carefully crafted simulated data so that there only exist non-trivial solutions.' However, it does not provide concrete access information (link, DOI, citation) for this simulated data to be publicly available or open. |
| Dataset Splits | No | The paper mentions using 'simulated data' but does not provide specific details on train/validation/test splits, percentages, or predefined partitioning methodology. |
| Hardware Specification | Yes | All experiments were run on Intel Xeon CPU E5-4657L v2 @ 2.4GHz using MATLAB s Optimization Toolbox. |
| Software Dependencies | No | The paper states 'using MATLAB s Optimization Toolbox' but does not specify the version numbers for either MATLAB or the Optimization Toolbox. |
| Experiment Setup | No | The paper describes characteristics of the simulated data used for experiments, such as 'tree depth', 'task length', and 'DAG depth', but does not provide specific details about the experimental setup for the solvers, such as hyperparameters or system-level training settings. |