@inproceedings{d619f69eaac94aaaa9c8b5b4f77fa1d6,
title = "Load forecast of resource scheduler in cloud architecture",
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.",
keywords = "Cloud architecture, Forecast mechanism, Machine learning, Virtual machine",
author = "Huahui Lyu and Ping Li and Ruihong Yan and Anum Masood and Bin Sheng and Yaoying Luo",
year = "2016",
month = dec,
doi = "10.1109/PIC.2016.7949553",
language = "English",
series = "PIC 2016 - Proceedings of the 2016 IEEE International Conference on Progress in Informatics and Computing",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "508--512",
editor = "Yinglin Wang and Yaoru Sun",
booktitle = "PIC 2016 - Proceedings of the 2016 IEEE International Conference on Progress in Informatics and Computing",
note = "4th IEEE International Conference on Progress in Informatics and Computing, PIC 2016 ; Conference date: 23-12-2016 Through 25-12-2016",
}