The Cloud Computing Load Forecasting Algorithm Based on Kalman Filter and ANFIS
Jian Sun, Yi Zhuang
Available Online March 2016.
- https://doi.org/10.2991/icmmct-16.2016.114How to use a DOI?
- cloud computingï¼Œload forecastingï¼ŒKalman Filterï¼ŒANFIS
- 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.
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 -