December 01, 2008
BOSTON, Dec. 1 -- BEZ Systems, an innovator in the emerging market for predictive application and database performance management solutions, today announced it has received an extension of its series D financing round from proven and successful software industry investors led by Ascent Venture Partners, with Velocity Equity Partners and The Massachusetts Technology Development Corp. also participating. The funds are being used to increase product virtualization support and channel development.
BEZ's fundamental belief is that predictive analytics can take the guesswork out of ensuring acceptable application and database performance. Firefighting, reacting to service faults and playing the "blame game" are wasteful and provide no business value in good times and are unlikely to be tolerated in difficult times. Now that IT is customer facing, the majority of customer interactions, directly or indirectly, depend on good performance and throughput of key business applications. In tough times, service delivery is not the place to cut corners.
"Additional funding at this time reflects the confidence that our venture partners have in the value of the BEZ solution and market acceptance," said F. Daniel Haley, BEZ president and CEO. "We believe the combination of a slow economy and increasing exposure of customer facing applications will move companies to be more proactive and take advantage of the efficiencies, cost reduction and management focus inherent in predictive analytics."
Analytics can predict future service faults while "what-if" predictive technology helps insulate service delivery from constant changes such as mergers, acquisitions, service consolidations, capacity shifts, new applications or seasonal and event spikes.
About BEZ Systems
Since 1993, BEZ Systems (www.bez.com/) has been applying advanced analytics and modeling technology to IT performance issues. Analytics aggregate current and historic performance data into meaningful business workloads and modeling provides IT management with historic, current and over-the-horizon views of workload service levels. The results provide IT decision-makers with the time and information to make informed business decisions, deploy resources more efficiently, and ensure that service delivery to the business is insulated from the chaotic effects of constant change.
The ever-growing complexity of scientific and engineering problems continues to pose new computational challenges. Thus, we present a novel federation model that enables end-users with the ability to aggregate heterogeneous resource scale problems. The feasibility of this federation model has been proven, in the context of the UberCloud HPC Experiment, by gathering the most comprehensive information to date on the effects of pillars on microfluid channel flow.
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