Romonet’s Prognose suite is available in server-based and software-as-a-service (SaaS) models. It analyzes all the different metrics that go into building a data center--such as the kind of servers, processors, and other hardware that will be used, as well as the type and volume of workloads to be run and the source and type of electrical power--and predicts the total cost of ownership. It's meant to perform predictive modeling throughout the life of the data center, not just in the planning.
Data center predictive modeling (DCPM) takes much of the guesswork out of data center planning, says Liam Newcombe, co-founder and CTO for Romonet. Currently, different disciplines within an organization--such as electrical engineers, mechanical engineers, IT engineers and business managers--have their own approaches to planning, and each speak their own language. When they present their ideas to the person who has to approve the project, he or she can't always make sense of what they're telling him.
"We want to bring the whole thing together so that we can capture the expertise of your mechanical people, your electrical people and your IT people, and present that to finance and hopefully have something that is greater than the sum of its parts," Newcombe says.
Demand for data center modeling is driven by pressure to wring costs out of data center operations through energy efficiency and by regulation intended to reduce carbon emissions, says Zahl Limbuwala, co-founder and CEO of Romonet. Data center managers have to watch their power usage effectiveness (PUE), a ratio of the amount of energy coming into a data center building to the amount of energy being used by the IT assets. The closer the ratio is to 1:0, the better.