Welcome to the InfoSci Platform
Reference Hub6
A Self-Adaptive Prediction Algorithm for Cloud Workloads

A Self-Adaptive Prediction Algorithm for Cloud Workloads

Li Mao, Deyu Qi, Weiwei Lin, Chaoyue Zhu
Copyright: © 2015 |Volume: 7 |Issue: 2 |Pages: 12
ISSN: 1938-0259|EISSN: 1938-0267|EISBN13: 9781466676664|DOI: 10.4018/IJGHPC.2015040105
Cite Article Cite Article

MLA

Mao, Li, et al. "A Self-Adaptive Prediction Algorithm for Cloud Workloads." IJGHPC vol.7, no.2 2015: pp.65-76. http://doi.org/10.4018/IJGHPC.2015040105

APA

Mao, L., Qi, D., Lin, W., & Zhu, C. (2015). A Self-Adaptive Prediction Algorithm for Cloud Workloads. International Journal of Grid and High Performance Computing (IJGHPC), 7(2), 65-76. http://doi.org/10.4018/IJGHPC.2015040105

Chicago

Mao, Li, et al. "A Self-Adaptive Prediction Algorithm for Cloud Workloads," International Journal of Grid and High Performance Computing (IJGHPC) 7, no.2: 65-76. http://doi.org/10.4018/IJGHPC.2015040105

Export Reference

Mendeley
Favorite Full-Issue Download

Abstract

It is difficult to analyze the workload in complex cloud computing environments with a single prediction algorithm as each algorithm has its own shortcomings. A self-adaptive prediction algorithm combining the advantages of linear regression (LR) and a BP neural network to predict workloads in clouds is proposed in this paper. The main idea of the self-adaptive prediction algorithm is to choose the better prediction method of the future workload. Some experiments of prediction algorithms are conducted with workloads on the public cloud servers. The experimental results show that the proposed algorithm has a relatively high accuracy on the workload predictions compared with the BP neural network and LR. Furthermore, in order to use the proposed algorithm in a cloud data center, a dynamic scheduling architecture of cloud resources is designed to improve resource utilization and reduce energy consumption.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.