@inproceedings{56cc5a027c73412ea0e7dd22a8b821fc,
title = "A novel VM workload prediction using grey forecasting model in cloud data center",
abstract = "In recent years, the resource demands in cloud environment have been increased incrementally. In order to effectively allocate the resources, the workload prediction of virtual machines (VMs) is a vital issue that makes the VM allocation more instantaneous and reduces the power consumption. In this paper, we propose a workload prediction method using Grey Forecasting model to allocate VMs, which is the first string in the research field. Firstly, we utilize the time-dependent of workload at the same period in every day, and forecast the VM workload tendency towards increasing or decreasing. Next, we compare the predicted value with previous time period on workload usage, then determine to migrate which VM wherein the physical machine (PM) for the balanced workload and lower power consumption. The simulation results show that our proposed method not only uses the fewer data to predict the workload accurately but also allocates the resource of VMs with power saving.",
keywords = "cloud data center, grey interval forecasting, power consumption, VM migration, workload prediction",
author = "Jheng, {Jhu Jyun} and Tseng, {Fan Hsun} and Chao, {Han Chieh} and Chou, {Li Der}",
year = "2014",
doi = "10.1109/ICOIN.2014.6799662",
language = "???core.languages.en_GB???",
isbn = "9781479936892",
series = "International Conference on Information Networking",
publisher = "IEEE Computer Society",
pages = "40--45",
booktitle = "International Conference on Information Networking 2014, ICOIN 2014",
note = "2014 28th International Conference on Information Networking, ICOIN 2014 ; Conference date: 10-02-2014 Through 12-02-2014",
}