| Title | Cloud Storage I/O Load Prediction Based on XB-IOPS Feature Engineering |
| Publication Type | Conference Paper |
| Year of Publication | 2022 |
| Authors | Liang, Chenjun, Deng, Li, Zhu, Jincan, Cao, Zhen, Li, Chao |
| Conference Name | 2022 IEEE 8th Intl Conference on Big Data Security on Cloud (BigDataSecurity), IEEE Intl Conference on High Performance and Smart Computing, (HPSC) and IEEE Intl Conference on Intelligent Data and Security (IDS) |
| Keywords | block storage, cloud computing, Conferences, feature engineering, i-o systems security, load balance, performance evaluation, Predictive models, pubcrawl, Scalability, Schedules, security, System performance, Xgboost |
| Abstract | With the popularization of cloud computing and the deepening of its application, more and more cloud block storage systems have been put into use. The performance optimization of cloud block storage systems has become an important challenge facing today, which is manifested in the reduction of system performance caused by the unbalanced resource load of cloud block storage systems. Accurately predicting the I/O load status of the cloud block storage system can effectively avoid the load imbalance problem. However, the cloud block storage system has the characteristics of frequent random reads and writes, and a large amount of I/O requests, which makes prediction difficult. Therefore, we propose a novel I/O load prediction method for XB-IOPS feature engineering. The feature engineering is designed according to the I/O request pattern, I/O size and I/O interference, and realizes the prediction of the actual load value at a certain moment in the future and the average load value in the continuous time interval in the future. Validated on a real dataset of Alibaba Cloud block storage system, the results show that the XB-IOPS feature engineering prediction model in this paper has better performance in Alibaba Cloud block storage devices where random I/O and small I/O dominate. The prediction performance is better, and the prediction time is shorter than other prediction models. |
| DOI | 10.1109/BigDataSecurityHPSCIDS54978.2022.00020 |
| Citation Key | liang_cloud_2022 |
Cloud Storage I/O Load Prediction Based on XB-IOPS Feature Engineering