Organizations can use AI for storage management and AIOps to learn where to make improvements in their storage infrastructure, but note the benefits and drawbacks.
The rise of AI has advanced storage management from storage resource management software to AIOps tools that automate much of the process.
AIOps enables monitoring, diagnostics, predictive analysis and prescriptive functions for storage infrastructure and applications. Essentially, AIOps can tell an organization what is happening with storage and why, what could happen and what to do about it.
By taking much of the manual work out of storage management, AIOps drives efficiencies and frees IT staff to work on other tasks. A variety of vendors provide AI for storage and storage management, which has advantages and disadvantages.
How AI storage management works
AIOps for storage uses machine learning to collect and analyze telemetry data. This information is turned into predictive analytics.
AIOps includes automation, performance management and service management and automates many of the decisions involved in scaling and securing storage systems. Additionally, AI can help with tasks such as storage planning, storage lifecycle management, root cause analytics and storage optimization.
Telemetry data is a key piece of AI storage management tools. This data is information collected through sensors from storage, servers and networking systems.
AI and machine learning analyze information gathered from these systems on hardware devices, OSes, applications and hypervisors. This helps to detect anomalous activity, such as poorly configured devices, surprising capacity growth or unusual throughput demands. These activities can be used for resource planning and optimizing storage performance.
AI storage management systems are often SaaS applications that perform the analysis on a public cloud. This enables those applications to compare that data to information collected from a wide range of systems, increasing their predictive capabilities. Several vendors, though, offer products that are specifically designed to run on premises.
The differentiation in AI-based storage management tools is in their functionality. Many such tools use AI to help with troubleshooting, root cause analysis and storage optimization. Some tools provide additional functionality, such as help with power consumption, application deployment and even hardware lifecycle management.
Most of the AI-enabled storage tools are proprietary in nature, but some storage vendors take advantage of APIs as a way of gathering data from third-party sources.
Before investing in an AI storage tool, consider how the system delivers alerts. Most tools display alerts on dashboards, but some use other mechanisms, such as text messages or email messages. Whatever the delivery method, the tool should filter out noise alerts so the organization can focus on what is important.
AI storage management options
Several vendors offer AI storage management software that is compatible with various systems and applications. Vendors also use AI storage management with their storage-as-a-service offerings.
Vendors and the systems and applications they cover include the following:
Dell Apex AIOps Infrastructure Observability. All Dell storage; PowerEdge servers; VxRail, PowerFlex and VxBlock converged and hyperconverged infrastructure (HCI); PowerProtect Data Domain and PowerProtect Data Manager data protection; and PowerSwitch and Connectrix networking.
HPE InfoSight. Alletra, Primera and Nimble storage; SimpliVity HCI; ProLiant and Apollo servers; and Synergy composable infrastructure.
IBM Storage Insights. All IBM block storage, switches, fabrics and VMware ESXi hosts -- IBM Storage Insights Pro, the paid subscription version, also covers IBM and third-party block and object storage.
Infinidat InfiniVerse. InfiniBox, InfiniBox SSA.
NetApp Active IQ. OnTap, Element, StorageGrid and SANtricity.
Pure Storage Pure1. FlashArray, FlashBlade and Portworx storage.
Advantages of AI storage management
AI-driven storage management removes much of the complexity and manual tasks of traditional storage resource management. Benefits include automated provisioning, intelligent data tiering and workload optimization. Dedicated storage personnel can spend less time monitoring and managing systems as a result. AIOps can be a big help for MSPs because it enables them to remotely manage many customers' storage.
By predicting future events based on current usage patterns, management systems can prevent problems that users may not otherwise anticipate. They also can advise users to add storage capacity and compute resources and complete other upgrades before performance becomes a problem. Users can configure the applications to automatically take action to prevent device failures or performance degradation, although IT shops may prefer to simply receive recommendations and then make those changes themselves.
Challenges of AI storage management
One issue with AI storage management systems is that they are often proprietary and usually work with only one vendor's products. For example, if an organization has a SAN from one vendor, another vendor's AI storage management system may not be compatible with it.
Another issue is the collection and analysis of data create more data -- a case of storage management creating a need for more storage. Over time, organizations must decide which data to safely discard.
Not all organizations can allow third parties to connect to their data centers. So-called dark sites cannot use SaaS-based analytics that collect and store data in public clouds or at the vendor's site. Vendors can use analytics software on local servers so the telemetry data doesn't travel, but these users lose some benefit of the analytics when their data isn't compared to that of their peers.
AI-based storage management will evolve as a direct result of AI technology reaching maturity.
Dark site or not, all users should ask their vendors how much information they collect outside of storage and how they guarantee that data remains anonymous and protected.
Since AI in storage management is still a somewhat new technology, algorithms will improve over time as more information is gathered.
The future of AI in storage management
The last few years have seen tremendous advances in AI and machine learning technologies. As such, AI-based storage management will evolve as a direct result of AI technology reaching maturity.
Storage vendors will likely use AI as a security tool. Enterprises might train AI monitoring, for example, to recognize the signs of a ransomware attack and help prevent a potential infection.
AI-based storage management tools will likely support intelligent backup and recovery capabilities. For example, AI could automatically identify the organization's most critical data, ensure that the data is backed up and prioritize this high-value data in the event a restoration becomes necessary.
AI in storage systems could also deliver self-healing capabilities. In doing so, AI might detect failing disks, corrupt sectors or similar problems and take corrective action to prevent data loss or system outages.
Brien Posey is a 22-time Microsoft MVP and a commercial astronaut candidate. In his over 30 years in IT, he has served as a lead network engineer for the U.S. Department of Defense and as a network administrator for some of the largest insurance companies in America.
Dave Raffo previously worked at TechTarget from 2007 to 2021 as executive news director and editorial director for its storage coverage.
