Romonet, a London-based company that makes predictive modeling software for data center management, has added an element of big data analytics to its software solution that already includes its proprietary predictive modeling engine.
The advantage of adding a big data architecture, with a NoSQL database at its core, is faster and more frequent predictions about the data center’s health. If there is a performance issue, the data center operator can identify and address it faster, potentially preventing customer downtime.
Using software to analyze data generated by devices on the data center floor for more efficient data center management is a growing field. Last year, Google revealed its quite sophisticated approach to this problem, using a “neural network” to analyze operational data generated by its data centers and make recommendations for improvements.
Typical data center monitoring and trend analysis tools act on events that have already happened, such as alarms, or historical trends, Romonet CEO Zahl Limbuwala, explained in a statement. Romonet’s software collects device data in real time, analyzes historical data, and can warn the operator of an upcoming problem.
“It can actually provide a warning when device performance starts to degrade or exhibit small but important early-warning signs of failure,” Limbuwala said.
Romonet’s software’s strength has traditionally been in using data about a data center to create models that provide comprehensive financial analysis of how well or poorly it performs and to predict what effects certain changes will have. The new features add real-time functionality for day-to-day data center management.
The company’s engineers have also spent time to improve the way their software discerns between useful data and low-quality data generated by monitoring devices. Romonet CTO Liam Newcombe said about 35 percent of data coming out of building management systems was “junk data.”
The upgraded solution cleans, verifies, and stores metered data in the NoSQL database. Data can be stored over a period of many years and used for trend analysis.