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HPE InfoSight: Cross-stack Analytics
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HPE InfoSight: Cross-stack Analytics

Accurate and action-oriented predictive analytics have long been the Holy Grail of IT management. Predictive analytics that bring together large amounts of real-time data with powerful analytical capabilities have the potential to provide IT managers with real-time, data-driven insights into the health and performance of their overall environment, enabling them to anticipate and remediate looming issues and optimize resource utilization. While these potential benefits have long been understood, it has only been recently that major innovations in cloud, Internet of Things (IoT), data science, and AI/machine learning have paved the way for predictive analytics to become a reality in the data center.

The IoT now enables companies to collect and monitor real-time sensory or operational data at the edge—whether in online financial systems, retail locations, or on the factory floor. This raw data is typically streamed to the cloud, where it can be tabulated and analyzed. Powerful advances in edge-to-cloud networks and global learning capabilities make the cloud an optimal location for the analytics to take place. Informed by data science and increasingly driven by AI and machine learning technologies, these analytics can help IT managers to monitor key system metrics and understand how well specific infrastructure elements—such as servers or storage—are performing.


But analytics that are focused on a single infrastructure element at a time can only go so far. Sure, it is helpful to monitor the health and performance of specific IT resources, such as CPU heartbeat or storage latency, but infrastructure resources do not operate independently or in isolation. Analytics must go beyond one dimension, and take into account how resources such as servers and storage interact with and depend on one another. This is especially critical in virtualized infrastructures, in which the interaction of virtual machines with hosts, networks and storage makes IT management even more challenging. Ideally, using the power of AI, analytics can cross these various layers of the IT stack to reveal the impact of resource interactions and interdependencies among all the layers. This would take analytics to a whole new level, transcending the limits of human intelligence to enable dynamic, multi-dimensional analysis of complex, virtualized IT environments.

Think about the implications of AI-driven, cross-stack analytics for IT management. For example, such a capability has the potential to transform technical support from a reactive, always-playing-catch-up function to a proactive and forward-looking capability. In this scenario, built-in analytics are capable of connecting the dots between infrastructure layers to automatically anticipate, diagnose, and fix technical issues before they become major problems. Cross-layer analytics might also help to improve system performance by predicting looming configuration issues and recommending changes to address them.


One product—HPE InfoSight—is already embracing these possibilities, fast-forwarding to bring AI-driven, cross-layer analytics to virtualized environments today. HPE InfoSight has proven its value in delivering predictive storage analytics to customers for many years, while extending its capabilities across the infrastructure stack. In this piece we’ll explore the key characteristics that customers should look for in an analytics solution for virtual infrastructure and then look at the HPE InfoSight architecture and its capabilities, and how they are helping customers transform IT management in virtualized environments today. Specifically, we will demonstrate how one customer uses cross-stack analytics delivered by HPE InfoSight to save tremendous time and money in their HPE 3PAR Storage environment.

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Publish date: 06/28/18

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