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 [1].
Modeling Multi-Attribute Demand for Sustainable Cloud Computing with Copulae
Authors: Maryam Ghasemi, Benjamin Lubin
IJCAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We investigate several choices for both models by studying a public data set of Google datacenter usage. ... We then validate our approach based on a public data set from Google [Reiss et al., 2011] that contains 29 days of workload data from a large production cluster. |
| Researcher Affiliation | Academia | Maryam Ghasemi Computer Science Department Boston University EMAIL Benjamin Lubin School of Management Boston University EMAIL |
| Pseudocode | Yes | Algorithm 1: The Bootstrap Kolmogorov-Smirnov goodness-of-fit test for a hypothesized family of distributions against observed data. |
| Open Source Code | No | The paper discusses the use of third-party R packages ('copula' and 'CDVine') but does not state that the code for the methodology developed in this paper is publicly available. |
| Open Datasets | Yes | We then validate our approach based on a public data set from Google [Reiss et al., 2011] that contains 29 days of workload data from a large production cluster. |
| Dataset Splits | No | The paper describes data filtering and processing steps but does not specify explicit training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not provide specific hardware details (like CPU/GPU models, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions using Matlab, R, and the 'copula' and 'CDVine' R packages, but does not provide specific version numbers for any of these software components. |
| Experiment Setup | Yes | Next, to interpolate between the job types (modes) in the data, we perform a bivariate kernel-density smoothing with a Normal kernel and a bandwidth of 0.4 and 0.3 for memory and CPU respectively. ... In our analysis we let b = 1000. |