Load forecast of resource scheduler in cloud architecture

Huahui Lyu, Ping Li, Ruihong Yan, Anum Masood, Bin Sheng, Yaoying Luo

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

1 Citation (Scopus)

Abstract

Cloud architectures have become increasing common in the IT industry and academic circle. However, most cloud architectures only focus on availability but ignore economic effectiveness. Based on Eucalyptus, this paper proposes an effective way to balance high availability and economic profits. We designed a forecast mechanism using three new modules: the forecast module, adjustment module, and collection module. The forecast module uses a set of machine learning techniques to improve forecast accuracy. We carried out a comparative experiment and the experimental results prove the efficiency of the proposed forecast mechanism.

Original languageEnglish
Title of host publicationPIC 2016 - Proceedings of the 2016 IEEE International Conference on Progress in Informatics and Computing
EditorsYinglin Wang, Yaoru Sun
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages508-512
Number of pages5
ISBN (Electronic)9781509034833
DOIs
Publication statusPublished - 15 Jun 2017
Externally publishedYes
Event4th IEEE International Conference on Progress in Informatics and Computing, PIC 2016 - Shanghai, China
Duration: 23 Dec 201625 Dec 2016

Publication series

NamePIC 2016 - Proceedings of the 2016 IEEE International Conference on Progress in Informatics and Computing

Conference

Conference4th IEEE International Conference on Progress in Informatics and Computing, PIC 2016
Country/TerritoryChina
CityShanghai
Period23/12/1625/12/16

Keywords

  • Cloud architecture
  • Forecast mechanism
  • Machine learning
  • Virtual machine

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Information Systems
  • Health Informatics

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