Intelligent Virtual Machine Provisioning in Cloud Computing
Authors: Chuan Luo, Bo Qiao, Xin Chen, Pu Zhao, Randolph Yao, Hongyu Zhang, Wei Wu, Andrew Zhou, Qingwei Lin
IJCAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments show that UAHS performs much better than state-of-the-art competitors on two public datasets and an industrial dataset. UAHS has been successfully applied in Microsoft Azure and brought practical benefits in real-world applications. |
| Researcher Affiliation | Collaboration | Chuan Luo1 , Bo Qiao1 , Xin Chen1 , Pu Zhao1 , Randolph Yao2 , Hongyu Zhang3 , Wei Wu4 , Andrew Zhou5 and Qingwei Lin1, 1Microsoft Research, China 2Microsoft Azure, United States 3The University of Newcastle, Australia 4University of Technology Sydney, Australia 5Microsoft Office, China |
| Pseudocode | Yes | Algorithm 1: Algorithm for searching configuration Algorithm 2: Function Predict Algorithm 3: Function Optimize |
| Open Source Code | No | The paper does not provide a direct link to the source code for the described methodology or explicitly state that the code is open-source. It provides a link to public datasets. |
| Open Datasets | Yes | To study the performance of UAHS, we conduct extensive experiments on two public datasets5 [Cortez et al., 2017] collected from Microsoft Azure, dubbed Azure-2017 and Azure-2019. [...] 5https://github.com/Azure/Azure Public Dataset |
| Dataset Splits | No | The paper describes how instances are constructed from the datasets: 'we construct 42 instances by taking all sub-time series in length of 139 (i.e., 180 42 + 1) time stamps using the sliding window based time series analysis approach [Box et al., 2015], each of which has the demand on the last time stamp as label.' However, it does not specify a distinct training, validation, and test split for the overall UAHS system's evaluation, but rather evaluates performance 'across all instances'. |
| Hardware Specification | Yes | In this work, all experiments were conducted on a machine with Intel Xeon E5-2673 CPU and 256 GB memory, running GNU/Linux. |
| Software Dependencies | No | The paper mentions the operating system ('GNU/Linux') but does not provide specific version numbers for other key software dependencies like programming languages, libraries, or frameworks used in the experiments. |
| Experiment Setup | Yes | For UAHS, the hyper-parameter settings of max_iter and prob are set to 50 and 0.9, respectively. The effects of different hyper-parameter settings of max_iter and prob are discussed in Section 5.4. |