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Data Center Predictive Modelling

Data Center predictive modeling (DCPM) is an essential tool for all data center providers that are looking for solutions to the downtime issues. Predictive modeling is a computer model that analyzes the statistics of the physical components that are available in a data center and looks to predict the future behavior of the infrastructure in case the providers are considering building a new data center or expanding existing infrastructure. The evaluation is done based on the past and present performance and the impact any proposed modifications may have if any new IT equipment is installed in the data center. 
Computational fluid dynamics (CFD) is software that is used for analyzing and modeling airflow and temperature simulation and data centers operations efficiently without any risks. DCPM evaluates the cause and effect a data center will undergo before changes are implemented.
The objective of data center predictive modeling is to calculate the airflow distribution for the entire data center area from the floor level.It also has the ability to analyze the effectiveness of the cooling solutions at the rack and aisle level, locating areas where cooling is more or less than the requirements. It also points out areas where hot air is reducing the impact of cooling, model airflows under raised floor height etc. making it easier for the data-center designers and operators. DCPM also is important since the performance of infrastructure changes with the wear and tear of machinery. It evaluates the impact of any upgraded infrastructure will have on the performance of the data center.
Data center predictive modeling was first initiated in the year 2011 by a UK based private company Romonet that has its headquarters in Croydon offering cloud solutions to data centers. They came up with this concept of DCPM as a better analogy over doing a collection of real-time data. 

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