Load forecast of resource scheduler in cloud architecture

Yaoying LUO, Ping LI, Ruihong YAN, Anum MASOOD, Bin SHENG, Huahui LYU

Research output: Chapter in Book/Report/Conference proceedingChapters

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. Copyright © 2016 IEEE.
Original languageEnglish
Title of host publicationProceedings of the 2016 IEEE International Conference on Progress in Informatics and Computing
Place of PublicationBeijing
PublisherInstitute of Electrical and Electronics Engineers, Inc
Pages508-512
ISBN (Print)9781509034840, 9781509034833
Publication statusPublished - 2016

Citation

Lyu, H., Li. P., Yan, R., Masood, A., Sheng, B., & Luo, Y. (2016). Load forecast of resource scheduler in cloud architecture. In Y. Wang, & Y. Sun (Eds.), Proceedings of the 2016 IEEE International Conference on Progress in Informatics and Computing (pp. 508-512). Beijing: Institute of Electrical and Electronics Engineers, Inc.

Keywords

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

Fingerprint Dive into the research topics of 'Load forecast of resource scheduler in cloud architecture'. Together they form a unique fingerprint.