Cloud Resources Usage Prediction Using Deep Learning Models

Muhammad Johan Alibasa, Basem Suleiman, Abubakar Bello, Ali Anaissi, Qijing Yan, Shulei Chen

Research output: Chapter in Book/Report/Conference proceedingConference proceeding (ISBN)peer-review


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.

Original languageEnglish
Title of host publicationProceedings of the 2023 International Conference on Advances in Computing Research (ACR’23)
EditorsKevin Daimi, Abeer Al Sadoon
PublisherSpringer Cham
Number of pages10
ISBN (Electronic)978-3-031-33743-7
ISBN (Print)978-3-031-33742-0
Publication statusPublished - 27 May 2023

Publication series

NameLecture Notes in Networks and Systems
ISSN (Print)2367-3370
ISSN (Electronic)2367-3389


  • cluster data
  • cpu usage
  • deep learning models
  • memory usage
  • sequence models
  • utilization prediction


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