NASDAQ goes for SevOne

Makes the selection in order to monitor its global network with specialist low-latency tools

  • 10 years ago Posted in

If you run one of the best known global stock exchanges, and one that seems to concentrate much of its effort on high-tech industries, then ensuring your global network is up to scratch is probably a reasonably important task.

That could certainly apply to NASDAQ OMX, which runs the famous NASDAQ stock exchange. To help with just such a job the company has selected performance monitoring specialist, SevOne, to provide real-time datacentre performance management solutions to support the company’s global organisation.

SevOne’s core specialism is in monitoring and reporting on ultra-low latency networks , which has made it attractive to a number of organisations in the financial services market. In addition to NASDAQ, SevOne currently monitors infrastructure for seven of the top 13 global investment banks.

An all-in-one solution, SevOne detects and alerts on network and datacentre performance events before they impact business. The company has developed a next-generation technology called the SevOne Cluster that incorporates the latest principles behind distributed computing to address scalability concerns. It can monitor millions of objects across multiple technologies from a single pane of glass.

 “We deliver the world's most scalable performance monitoring platform to the world's most connected companies and NASDAQ OMX is a great addition to our portfolio of customers.”

NASDAQ has selected DevOne’s Performance Appliance Solution to monitor and report on its infrastructure across North America, Europe, and Asia-Pacific.

“In our business, it’s imperative that we understand application traffic and prevent performance issues before they impact critical network services,” said Lou Modano, Senior Vice President, Global Infrastructure Services, of the NASDAQ OMX Group. “As we evaluated vendors in this space, we found SevOne had a new way to approach the collection of performance data and prediction of potential service degradation.”

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