Predictive Job Scheduling under Uncertain Constraints in Cloud Computing
Authors: Hang Dong, Boshi Wang, Bo Qiao, Wenqian Xing, Chuan Luo, Si Qin, Qingwei Lin, Dongmei Zhang, Gurpreet Virdi, Thomas Moscibroda
IJCAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our extensive experiments on three public, industrial datasets shows that CUC has great potential for supporting high reliability in cloud platforms. |
| Researcher Affiliation | Collaboration | 1Microsoft Research, China 2Microsoft Azure, United States 3The Ohio State University, United States {hangdong, boqiao, v-wenxing, chual, siqin, qlin, dongmeiz}@microsoft.com {v-boshiwang, gurvir, moscitho}@microsoft.com |
| Pseudocode | Yes | Algorithm 1: Training Procedures of CUC; Algorithm 2: Prediction Method in CUC; Algorithm 3: Greedy Construction Method in CUC; Algorithm 4: Testing Procedures of CUC |
| Open Source Code | No | The paper states that the pre-processed dataset 'will also be publicly available', but it does not explicitly state that the source code for the proposed CUC methodology is publicly available or provide a link to it. |
| Open Datasets | Yes | The experiments in this work are conducted on three public datasets, which are all collected from Microsoft Azure. In particular, two public datasets are introduced in the literature [Cortez et al., 2017] and each of both contains the representative VM workload of Microsoft Azure across 30 consecutive days; the other dataset is described in [Hadary et al., 2020] and includes the representative VM workload of Microsoft Azure across 14 consecutive days. |
| Dataset Splits | Yes | Each of the three datasets is divided into a training set and a testing set. For the first 5 days out of the 14 days, we only use the hourly available capacity for fitting our prediction model. Then a sliding window is adopted to generate samples, with each sample containing past hourly capacity for 5 days (i.e., 120 data points for prediction), future capacity for T = 24 hours, and the job requests in the next 24 hours. |
| Hardware Specification | Yes | In this work, all experiments were conducted on a machine with Intel Xeon E5-2690 v4 CPU, 112GB memory and NVIDIA Tesla P100 GPU. |
| Software Dependencies | No | The paper mentions using 'Gurobi, a professional solver for linear and non-linear optimization problems' but does not specify a version number. It also mentions other methods like 'time series decomposition based forecasting approach (TSDec)', 'automatic autoregressive integrated moving average (Auto ARIMA)', 'long short-term memory (LSTM)', 'un-observed component model (UCM)', and 'Prophet', but does not provide specific version numbers for any libraries or frameworks used to implement them. |
| Experiment Setup | No | The paper describes the overall design of the CUC algorithm and mentions setting 'the number of BO steps as k = 50', but it does not provide specific hyperparameters like learning rates, batch sizes, or optimizer settings for the prediction models or other components of the experiment. |