Cloud-based predictive analytics seeing wider adoption
As flash arrays become more widely adopted within the enterprise, IT teams are looking for storage products that…
incorporate predictive analytics to help forecast trends, plan infrastructures and reduce overhead. Cloud-based predictive analytics uses machine learning and other advanced techniques to analyze telemetry data gathered throughout the storage stack, making it possible to proactively address storage issues before they arise.
Predictive analytics is a type of analytics that uses statistical methodologies to forecast specific outcomes based on patterns uncovered in historical and current data. The methodologies can include such advanced techniques as data mining, analytical queries, predictive modeling and machine learning.
Machine learning is particularly important because it automatically trains the predictive algorithms by constantly updating outputs when new data becomes available. As workloads evolve, predictive analytics relies on machine learning to effectively detect and diagnose developing issues and then act upon those issues with minimal intervention.
Predictive analytics and machine learning make it possible to address storage-related issues before they occur, while providing the intelligence necessary to forecast how and when to implement hardware and software changes.
To carry out these operations, a cloud-based predictive analytics product must continuously collect telemetry data from the flash arrays and their supporting infrastructures. The data can include details about IOPS, bandwidth, faults, latencies and other relevant information. The product applies predictive analytics and machine learning to both historical and current data, as it steadily evolves.
Applying predictive analytics to enterprise storage can offer a number of benefits when it comes to forecasting trends, planning infrastructure and reducing overhead. Although these categories overlap, they provide a useful way of understanding the goals of an effective predictive analytics tool.
Because a predictive analytics tool is able to forecast trends, it can take a proactive approach to addressing storage issues, identifying them before they occur and users are affected. The predictive analytics product can then take corrective actions or, if an issue cannot be resolved automatically, notify stakeholders with actionable insights. This approach can help reduce the amount of downtime and increase application availability and performance, while maximizing productivity and the user experience. The ability to forecast trends requires large quantities of data from multiple systems to properly analyze storage. For example, the Pure1 cloud management service uses the accumulated data from more than 10,000 connected arrays, collecting over 1 trillion data points per day. The service utilizes the Pure1 Meta AI engine to return analytical calculations about the health and functioning of storage and other components in the application stack. Hewlett Packard Enterprise (HPE) InfoSight provides a similar cloud management service. Built on the Nimble technologies acquired by HPE, InfoSight collects and analyzes hundreds of billions of sensor data points from more than 9,000 customer environments, capturing millions of sensor measurements every second. InfoSight continuously learns from the collected data in order to forecast, prevent and resolve problems. As InfoSight learns, its infrastructure gets smarter and improves, helping to better automate tuning and answer questions before they’re even asked. InfoSight was a key driver behind HPE’s decision to acquire Nimble. Real-time predictive analytics also fueled DataDirect Networks’ decision to acquire the assets of Tintri this year. Tintri Analytics, another cloud management service, also relies on a massive pool of telemetry data to deliver a predictive analytics product.
