Load forecast of resource scheduler in cloud architecture

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

Research output: Contribution to conferencePaper


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


Loads (forces)
Learning systems


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 International Conference on Progress in Informatics and Computing (PIC-2016), Shanghai Guang Dong Hotel, Shanghai, China.


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