Load forecast of resource scheduler in cloud architecture

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

Research output: Contribution to conferencePapers

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
Publication statusPublished - Dec 2016
EventThe 2016 IEEE International Conference on Progress in Informatics and Computing - Shanghai, China
Duration: 23 Dec 201625 Dec 2016

Conference

ConferenceThe 2016 IEEE International Conference on Progress in Informatics and Computing
Abbreviated titlePIC 2016
Country/TerritoryChina
CityShanghai
Period23/12/1625/12/16

Citation

Lyu, H., Li. P., Yan, R., Masood, A., Sheng, B., & Luo, Y. (2016, December). Load forecast of resource scheduler in cloud architecture. Paper presented at The 2016 IEEE International Conference on Progress in Informatics and Computing (PIC-2016), Shanghai Guang Dong Hotel, Shanghai, China.

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.