@inproceedings{f6712383cbe84390b4e774c746cb734b,
title = "Cloud Resources Usage Prediction Using Deep Learning Models",
abstract = "Modern cluster management systems have effectively evolved to deal with the increasing and diverse cloud computing demands. However, several challenges including low resource utilization, high power consumption are still present that can be solved with a precise real-time usage prediction. This prediction problem is complicated since the cloud workloads vary dynamically and there are nonlinear relationships between the usage, duration and jobs characteristics. Therefore, non-linear feature extraction methods including logarithm, encoder and several feature extraction methods were used in the past studies. Our study utilized several regression models and deep learning models including GRU, LSTM in univariate and multivariate settings to explore and extract highly-dimensional and highly-nonlinear relationship. Our experiments used Google Cluster Trace data v3 to perform prediction on duration, CPU and memory utilization.",
keywords = "cluster data, cpu usage, deep learning models, memory usage, sequence models, utilization prediction",
author = "Alibasa, {Muhammad Johan} and Basem Suleiman and Abubakar Bello and Ali Anaissi and Qijing Yan and Shulei Chen",
note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.",
year = "2023",
month = may,
day = "27",
doi = "10.1007/978-3-031-33743-7_36",
language = "English",
isbn = "978-3-031-33742-0 ",
series = "Lecture Notes in Networks and Systems",
publisher = "Springer Cham",
pages = "443--452",
editor = "Kevin Daimi and {Al Sadoon}, Abeer",
booktitle = "Proceedings of the 2023 International Conference on Advances in Computing Research (ACR{\textquoteright}23)",
edition = "1",
}