Proceedings of the 2016 4th International Conference on Machinery, Materials and Computing Technology

The Cloud Computing Load Forecasting Algorithm Based on Kalman Filter and ANFIS

Authors
Jian Sun, Yi Zhuang
Corresponding Author
Jian Sun
Available Online March 2016.
DOI
https://doi.org/10.2991/icmmct-16.2016.114How to use a DOI?
Keywords
cloud computing,load forecasting,Kalman Filter,ANFIS
Abstract
The load forecasting in the cloud computing is one of the most important technologies to ensure the maximize utilization of the system resource. Under the premise that the load is known in the next stage, the cloud computing center can assign the physical machines in advance, thereby reducing the waiting time of the task, and can also reduce the cloud computing center energy consumption. This paper proposed a load forecasting algorithm based on the Kalman filter and adaptive neuro-fuzzy inference system (ANFIS), obtained more accurate load sequence by the kalman filter eliminate observation error, used ANFIS to forecast the load sequence. The predicted results were compared with the original ANFIS algorithm, Autoregressive Integrated Moving Average (ARIMA) algorithm. The K-ANFIS algorithm had improved the prediction accuracy significantly compared with the other two algorithms.
Open Access
This is an open access article distributed under the CC BY-NC license.

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Volume Title
Proceedings of the 2016 4th International Conference on Machinery, Materials and Computing Technology
Series
Advances in Engineering Research
Publication Date
March 2016
ISBN
978-94-6252-165-0
ISSN
2352-5401
DOI
https://doi.org/10.2991/icmmct-16.2016.114How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Jian Sun
AU  - Yi Zhuang
PY  - 2016/03
DA  - 2016/03
TI  - The Cloud Computing Load Forecasting Algorithm Based on Kalman Filter and ANFIS
BT  - Proceedings of the 2016 4th International Conference on Machinery, Materials and Computing Technology
PB  - Atlantis Press
SP  - 565
EP  - 569
SN  - 2352-5401
UR  - https://doi.org/10.2991/icmmct-16.2016.114
DO  - https://doi.org/10.2991/icmmct-16.2016.114
ID  - Sun2016/03
ER  -