Abstract
Predictive modeling or Forecasting is to predict the future using (time series related or other) data we have in hand. Cloud Infrastructure as a solution facilitates pay-as-you-use execution environments that scale transparently to the user. There is continuous challenge of configuring cloud infrastructure to provide maximal performance while minimizing the cost of resources used. This disclosure focuses time series data modeling to predict business requirements of a Cloud Platform and its application to daily infrastructure management. Such prediction can guide system design and deployment decisions such as scaling, scheduling, and capacity planning. Combining best two models on minimum variance approach is applied. Combining Regression Model and ARIMA Model by assigning weights on minimum variance. With this predictive model, capacity can be planned with buffer which will save huge cost on infrastructure front. The combined hybrid model provides better accuracy with time series prediction compared to individual forecasting models.
Creative Commons License
This work is licensed under a Creative Commons Attribution-Share Alike 4.0 License.
Recommended Citation
INC, HP, "Hybrid Time Series Forecasting Model for Provisioning of Cloud Resources.", Technical Disclosure Commons, (May 14, 2024)
https://www.tdcommons.org/dpubs_series/7001