Datasets & Codes used in our papers.
#1. Dataset & Codes for Predicting Machine Failures
Background: This dataset is to implement the failure prediction using machine learning methods and AI approaches such as SVM, random forest, or deep learning algorithms. Besides the original dataset, I also provide two reports written by two visiting students when they performed a visiting-study in my lab in July 2019.
Huawei Huang, and Song Guo, “Proactive Failure Recovery for NFV in Distributed Edge Computing”, IEEE Communications Magazine, vol. 57, no. 5, pp. 131-137, March 2019
The original dataset can be also downloaded from https://bigml.com/user/czuriaga/gallery/dataset/587d062d49c4a16936000810
- The dataset after preprocessing:
- The related technique reports and codes from two visiting students:
#2. Dataset & Codes for Predicting Server Failures
Background: This dataset is used to predict the failures of server machines that occurred on a datacenter. The related published papers are as follows.
Huakun Huang, Lingjun Zhao, Huawei Huang, Song Guo, "
Machine Fault Detection for Intelligent Self-Driving Networks", IEEE Communications Magazine, Vol. 58 , Issue No. 1, pp. 40-46, January 2020 [RG-Page]
Huakun Huang, Shuxue Ding, Lingjun Zhao, Huawei Huang, et al., "Real-Time Fault-Detection for IIoT Facilities using GBRBM-based DNN", IEEE Internet of Things Journal, Oct. 21, 2019. DOI: 10.1109/JIOT.2019.2948396 [RG-Page]
- Original dataset and cleaned dataset:
- Processing codes:
#3. Dataset & Codes for MVCom, which is published in ICDCS 2021
Background: This code-and-dataset shows how we implement the algorithms used in our paper, including the proposed SE algorithm and other 3 baselines.
Huawei Huang, Zhenyi Huang, Xiaowen Peng, Zibin Zheng, Song Guo, “MVCom: Scheduling Most Valuable Committees for the Large-Scale Sharded Blockchain”, ICDCS, July 2021 [
RG-Page & PDF]