Conference Paper2011Cited by 32

Neural network and regression based processor load prediction for efficient scaling of Grid and Cloud resources

Md Toukir Imam, Sheikh Faisal Miskhat, Rashedur M Rahman, M Ashraful Amin

14th International Conference on Computer and Information Technology (ICCIT 2011)

IEEE, pp. 333–338

DOI: 10.1109/iccitechn.2011.6164809URL

Abstract

An effective and efficient resource allocation policy could benefit the cloud environment by saving cost. To support the continuous load increase, the cloud platform needs to create new virtual machines. However, substantial amount of time is required for the creation and the setup of a virtual machine. Therefore, allocating resources in advance based on prediction models could improve the quality of the service of the cloud platform. In this paper we present time delay neural network and regression methods for predicting future workload in the Grid or Cloud platform. We use real world workload traces to test the performance of our algorithms. We also present an overall evaluation of this approach and its potential benefits for enabling efficient auto-scaling of Cloud user resources.

Keywords

  • auto-scaling
  • time-delay neural networks
  • workload traces
  • grid computing
  • virtual machine provisioning
  • Regression analysis
  • cloud resource allocation
  • resource cost reduction

CCDS Authors

Prof. M Ashraful Amin, PhD
M Ashraful Amin, PhD

Prof. M Ashraful Amin, PhD

  • Founder Director, CCDS
  • Director, AI/ML, HCI & DS Wings

Professor

+1 more affiliation

Department of Information Sciences and Technology

George Mason University, USA

Published at

ACL, CHI, UbiComp, ECAI, WACV, PAKDD, IJCNN, ICPR, ICIP, HCII, CLEF, BLP, Scientific Reports, JMIR, IEEE Access, Smart Agricultural Technology, Frontiers in Computational Neuroscience, Diabetes Research and Clinical Practice, PLOS ONE

Research Interests

Machine Learning, Cognitive & Vision Science, Cybernetics, Surveillance & Security, ICT in Education, Health, & Agriculture, Human-Computer Interaction, Internet of Things, Robotics

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