Predictive analytics, machine learning to boost uptime, efficiency in data centres

Predictive analytics and machine learning let enterprises automate system administration, maintenance and problem resolution tasks, says Sascha Schmidt-Ries, HPE storage product manager at Tarsus Distribution.

Johannesburg, 10 Jul 2018
Read time 2min 30sec

Use of advanced predictive analytics and machine learning solutions in the data centre will enable organisations to vastly simplify operations, improve the support experience and bolster uptime across their enterprise infrastructures. The technology should be an important consideration for end-users as they shop for their data centre platforms.

That's according to Sascha Schmidt-Ries, HPE storage product manager at Tarsus Distribution, who says predictive analytics and machine learning enable enterprises to automate system administration, maintenance and problem resolution tasks. The solutions use historical and real-time data to predict issues and resolve problems, in turn reducing downtime and maximising performance.

"We can expect a race among enterprise vendors to position themselves as leaders in this space," says Schmidt-Ries. "The introduction of predictive intelligence and machine learning is the key to driving self-management and operational efficiency in the data centre. Users want the ability to manage and troubleshoot the entire infrastructure from a single, intelligent platform."

Schmidt-Ries notes that Nimble Storage's InfoSight telemetry analytics engine ranked high among the reasons that Hewlett-Packard Enterprise (HPE) acquired the company for $1 billion in 2017. HPE has extended the Nimble Storage Infosight technology from Nimble's flash storage solutions to the HPE 3PAR range from January 2018.

HPE InfoSight lets companies monitor customer-deployed infrastructure from the cloud, as well as apply machine learning and predictive analytics to simplify operations. HPE InfoSight collects data from sensors across storage arrays and their surrounding infrastructure, then aggregates, analyses and correlates this data to predict and prevent issues.

HPE claims that InfoSight automatically detects 90% of all issues within a customer's infrastructure and resolves over 85% of them. This dramatically reduces the amount of time and effort the IT team spends on support activities, freeing up time for them to spend on innovation rather than just focusing on keep the lights on.

Data collected by the platform shows 86% of problems are automatically resolved before customers even realise there is an issue. What's more, 54% of the problems resolved are outside of storage. A key element of the platform is that it learns to predict issues in other systems after it detects them in one system.

HPE InfoSight remediates issues spanning the entire infrastructure stack from storage to virtual machines and applies machine learning to predict and advise customers on future capacity, performance, and bandwidth needs. "With this sort of technology, we are moving to the era of the autonomous data centre," Schmidt-Ries says.

"This means fewer support cases, faster resolution of complex problems and lower operational spending. The IT department can now focus on adding value to the business and leave more of the mundane troubleshooting and remediation to intelligent, automated systems."

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