Every enterprise storage briefing now includes a discussion of infrastructure analytics. For some vendors, this is a relatively new endeavor. Other vendors have woven analytics into the design of their company and products from day one. As companies evaluate enterprise storage offerings, they should pay careful attention to these infrastructure analytics and how they fit with their own needs, abilities, and culture.
COVID-19 Resulted in Unexpected Infrastructure Demands
If you have been paying attention to the headlines, you know that the pandemic has resulted in dramatic increases in the use of online collaboration tools, including Microsoft Teams, Zoom Meetings, Google Meet, and others. Many enterprises have also implemented or dramatically expanded virtual desktop infrastructures to support employees newly working from home. As a former IT director myself, I have thought about the value of these advances in infrastructure analytics in responding to these unexpected infrastructure demands.
Intelligent Infrastructure Now, Autonomous Infrastructure Future
Multiple enterprise storage vendors are now offering what we can reasonably describe as intelligent infrastructure. Their product roadmaps show we are on the path to automated enterprise infrastructure. In this article, we look briefly at HPE Nimble Storage, DDN’s Tintri VMstore, Hitachi Vantara, and Huawei as four exemplars of this journey.
Cloud-based Analytics Integrated with Proactive Support
HPE Nimble Storage provides comprehensive cloud-based InfoSight predictive analytics capabilities that it tightly integrates with proactive support processes. HPE InfoSight has app-aware intelligence that considers real application behavior and makes recommendations driven by AI. The maturity of InfoSight results in the complete automation of Level 1 and 2 support, with 86% of problems predicted and auto-resolved—a distinguished capability unmatched by any other storage vendor.
Beyond checking all the boxes in the Proactive Support and Predictive Analytics category, the “fix it once for everyone” philosophy behind this integration of analytics and support has resulted in six nines of availability across the installed base and avoided hundreds of high severity incidents.
Analytics Integrated into the Storage Operating System
Tintri VMstore focuses on consolidating huge numbers of virtualized workloads on a single storage infrastructure. It embeds analytics into its storage operating system and uses those to manage the performance of every workload dynamically. Tintri was perhaps the first vendor to offer integrated cross-stack analytics to make performance bottlenecks visible to infrastructure managers.
Analytics Alongside the Array
Hitachi Vantara offers its Ops Center, a multi-faceted operations and management software package. Ops Center runs on-premises in VMs or on a dedicated server. The goal of Hitachi Vantara for Ops Center is to maximize application performance. It applies AI to automate provisioning, data reduction, data migrations, and other data center workflows.
Many enterprises want to gain the benefits of automation and analytics, but their IT staff lack experience and their resources are limited. Hitachi Vantara addresses this need by offering a low cost “Automator Starter Pack.” This service helps the client to implement the software and then work through the process of automating two workflows with the customer across a period of approximately 90 days. The result is a quantified return on investment and transfer of knowledge to enterprise IT staff that will enable them to automate additional data center workflows.
Analytics Embedded in the Array via AI Chips
Huawei focuses its analytics on enabling autonomous infrastructure. Huawei’s newest OceanStor arrays incorporate its own Ascend 310 AI chip. The Huawei AI chip is based on machine learning frameworks. It understands and actively analyzes the I/O rules of multiple application models, and dynamically adjusts to I/O activity. Huawei claims the AI chip can improve the read cache hit ratio by 50%, and shorten batch processing latency from 300μs to 150μs.
Huawei supplements its AI chips with cloud-based analytics. It offers proactive support and predictive analytics via its eService Intelligent Cloud Management System, which relies on periodic collection and uploads of operations data, including alarms, configuration data, performance data, system logs, and disk information. This data enables intelligent planning, automated provisioning, monitoring, and end-to-end optimization.
Getting from Here to There
Integrating analytics into enterprise data center operations is a journey. The vendors mentioned in this article have taken four different paths on that journey. Enterprises should give careful attention to the infrastructure analytics provided by these solutions, and how well each offering fits with their own needs, abilities, and culture.
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