(and free up your IT staff so they can focus on the business)

It’s an understatement to say that managing infrastructure is not easy. If you are like most IT professionals, you’ve probably spent a great deal of time dealing with issues that disrupt your applications and business availability. You’ve likely also spent hours and hours manually tuning your infrastructure. Dealing with issues and the need for manual tuning bring headaches and frustration, as well as consume valuable time. Unfortunately, as the number of applications and reliance on infrastructure grow, so do these challenges and the headaches they cause. So what can you do?

Artificial Intelligence (AI) is helping IT professionals predict and prevent potential issues across their infrastructure, optimize performance, and ensure efficient use of resources – essentially creating autonomous infrastructure that can be managed with minimal human intervention. This results in tremendous labor savings, more efficient use of time and resources, and optimally tuned infrastructure.

In this blog post, we’ll look at the importance of AI for autonomous infrastructure and explore solutions from HPE that pave the way so that IT can focus on creating value for the business, instead of being caught in “break-fix-tune-repeat” routine.

Roadblocks to a Better Tomorrow

The way to the future is paved with data, which sits at the very heart of today’s digital transformation. To accomplish that shift, businesses must have uninterrupted access to data for their growing stable of digital applications. But that can be difficult to achieve, given the complexity of modern infrastructure and the many demands placed on limited IT resources. Try as they might, IT is held back by the need to react to a continuously repeating loop of manual tasks. Here’s why:

  • Traditional monitoring and support is antiquated: While monitoring tools can be helpful in troubleshooting environments, relying on them means spending countless hours analyzing log files to derive some sort of actionable insight. If that becomes too cumbersome, IT will look to their vendors for support, but doing so means managing the back-and-forth of multi-tiered escalations, which takes time that IT doesn’t have. This approach doesn’t work anymore. To move forward, IT needs a new and improved way to manage and support infrastructure. A solution that can predict problems and prevent them before they occur can empower IT staff to focus on more valuable initiatives.
  • Optimizing performance is a guessing game: As workloads constantly change, manually fine-tuning infrastructure can become tiresome. Ensuring optimal performance for applications requires human intervention, but that’s just not the way the world works anymore. Many companies solve this by overprovisioning, but why pay more than you need? And that doesn’t always guarantee improved performance. Not knowing whether you should move an application from an all-flash array to a hybrid or whether resizing a volume is needed results in tremendous costs. But what if IT could get recommendations on what to do, and when to do it, to optimize performance and available resources?
  • General purpose analytics fall short: Descriptive analytics merely report system metrics. This is not terribly valuable because it does not take into account the behavior of other systems to detect and diagnose potential issues. Moreover, these analytics tend to be siloed by device, which then have to be aggregated and cross-correlated. In addition, general-purpose analytics only go so deep; they are missing the deep domain experience of predictive modeling, which understands all the different parameters within each system. Combining domain expertise with AI helps predict (and therefore avoid) the most difficult and potentially damaging issues.

Ideally, to remove the challenges of infrastructure management, IT would have tools that provide predictive actionable intelligence about potential problems before they arise, as well as how to optimize environments based on granular insight into the underlying workloads and resources.

What we want to achieve is autonomous operations without the need for time-consuming manual intervention. This means not only knowing what changes should occur in advance of a problem in order to avoid it (as well as to optimize the operating environment), but also how to accomplish these ongoing tasks without taking valuable time away from our people.

The Path to an Easier Future

The good news is that infrastructure driven by AI can overcome all of the challenges that are the result of outdated tools and solutions, which can no longer support today’s digital business. Essentially, AI monitors your infrastructure, learns what is happening from a global installed base, and uses what it learns to predict and prevent issues from arising. This takes the guesswork – and a lot of time – out of infrastructure management.

Here’s how it works. Essentially, AI-powered infrastructure follows a five-step framework:

  • Observe: By monitoring all the systems in an installed base, AI learns the ideal operating environment for each workload and application. Abnormal behavior is identified through analysis of the underlying I/O patterns and configurations.
  • Learn: Globally connected system telemetry creates all the data necessary for machine learning to vastly accelerate the understanding of the AI.
  • Predict: AI learns to predict issues and determine whether other systems might be prone to them. It can also model and tune application performance for new infrastructure.
  • Recommend: Using predictive analytics, the AI makes the best recommendations, eliminating the need for IT professionals to pour over decisions.
  • Act: Moreover, these decisions can be applied automatically, minimizing manual labor and human intervention.

HPE InfoSight is an AI platform for autonomous infrastructure that collects and analyzes millions of sensor data points every second from HPE’s globally connected installed base. This data informs the platform’s predictive analytics and recommendation engines. Let’s take a closer look.

Predictive Analytics Lead the Way

HPE InfoSight’s predictive analytics can be used across the lifecycle of the entire infrastructure. For example, for planning, the solution anticipates the needed performance and resources and continuously updates this model for better accuracy. As arrays are deployed, HPE InfoSight looks for potential indicators of problems and solves them automatically. When detecting a new issue, the platform can prevent other systems from experiencing it in the future. As a result, IT benefits from an accurate prediction for what will be needed in the way of capacity, performance, and bandwidth so mission-critical applications and services are running at optimal performance.

Automated Recommendations Make Managing Infrastructure Easy

But for infrastructure to be truly autonomous, analytics aren’t enough on their own. Companies also need automated recommendations to optimize their environments. HPE InfoSight’s recommendation engine can suggest how to solve problems before they occur, enhance performance, and optimize available resources. Because of its advanced machine learning, the recommendation engine makes suggestions for performance improvements based on I/O workload patterns to determine which variables will work best.

Imagine, Infrastructure that no longer needs constant human intervention, manual tuning, and tiresome troubleshooting. It’s the way forward. And, with the power of predictive analytics and recommendations powered by artificial intelligence, you can now have infrastructure that manages itself, fixes issues proactively and automatically, and continually optimizes performance. That’s the power of AI – and it’s here today.

For further information on how you can leverage AI to enable an autonomous infrastructure, please visit veristor.com/datacenter/enterprise-storage/