Romonet adds more smarts to its data center predictive modeling software

Predictive modeling tool can take into account hardware, workloads, the climate, unexpected behavior – in an effort to assess efficiencies before breaking ground on a new data center.

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Modeling tools to help data center managers better understand the inefficiencies in their operations – such as the effectiveness of cooling within the racks and aisles – continue to evolve. There are a number of options out there, as well; everything from computational fluid dynamics modeling to capacity planning, modeling and performance management software and more recently predictive modeling to help forecast the performance of a data center into the future.

It’s the latter that is the basis of Romonet’s software, which a year into the unveiling of its predictive modeling software for the data center has just added new functionality to the suite. When Romonet introduced its predictive modeling software last year, it was well received chiefly because it was one of the first on the market to help organizations analyze data center efficiencies and even total cost of ownership while the data center is still on the drawing board.The software does so by assessing the various metrics involved, including the hardware, the volume of workloads, etc.

The Prognose 2.0 suite now includes three new products that factor in new metrics: Prognose energy, Prognose economics and Prognose enterprise. Prognose energy is designed to help users evaluate energy efficiency and power consumption across the all the components of a data center configuration. Prognose economics helps users evaluate capital expenditure and operational costs, and Prognose enterprise helps evaluate IT equipment configurations, environmental conditions, energy consumption and capacity loading on the energy efficiencies and operational costs related to the lifecycle of the data center.

New features within the suite let users perform simulations over a full Typical Meteorological Year (TMY) to help analyze conditions based on climate and actual or intended geographical location. The new Node Level Resilience (NLR) feature lets users rapidly model impact of control system changes in order to reduce the risk of an unexpected behavior impacting data center availability and uptime. There are also new finance-oriented outputs, and a new Cash-flow Reporting (CFR) and charting capability designed to illuminate operating costs month-to-month for improved Net Present Value (NPV) comparisons and lifetime costing analysis.

For you data center managers out there, do any of you rely on modeling tools as you assess your operations? And, what do you think of the potential in these predictive modeling tools like Prognose 2.0?

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