Hyperconverged Infrastructure

The Intersection of Hyperconvergence and Artificial Intelligence

Artificial intelligence (AI) and hyperconverged infrastructure (HCI) are both hot topics today. Many organizations utilize ML (ML) and AI to boost application performance and manage large amounts of analytical data. Despite this, some believe AI to be an intrusive technology that eliminates jobs and decreases the level of human interaction.

This is more perception than reality. When coupled with HCI, productivity and efficiency increase substantially. For many organizations, this is becoming the standard for building quick-to-market and scalable datacenter solutions. While some eschew the many benefits of HCI over traditional infrastructure due to cost and budget constraints, there are far more organizations taking advantage of what HCI offers. The intersection of AI and HCI has a significant impact in the datacenter. This impact optimizes HCI storage, manages workload demands, and optimizes application workloads.

Leveraging AI to Optimize HCI Storage

Storage is an integral part of the HCI architecture. If HCI storage is not properly optimized, it can create performance bottlenecks and keep regularly-accessed blocks of data from being presented in a timely fashion. This is where AI steps in to answer the call. AI has the capability of analyzing large amounts of data quickly. With AI and ML running in the background of an HCI deployment, it can analyze which data is being accessed more frequently than other data.

Additionally, AI can present the HCI management layer with options to automatically implement changes on the fly, or preserve the option for the administrator to make the call. In most cases, it’s more beneficial to understand the performance baseline of your HCI environment and trust the AI and ML processes to make the right call. ML predictability engines in the latest HCI solutions allow for intelligent analysis of your storage workload to optimize the storage for peak performance.

Keeping Up With Changing Workload Demands

Depending on the organization, workloads can be very diverse. Certain applications carry a heavy workload, such as email and file sharing services, while other applications are more dormant. With the application of AI and ML engines, diverse workload allocation is no longer an issue that keeps administrators up at night. Automation is the real magic of AI, and when applied to HCI workloads, it becomes even more resourceful.

For example, AI has the capability to recognize workload trends and prepare HCI resources in advance of a possible spike in workload demand. When HCI and AI are combined, resource management becomes much more efficient, and performance increases without the added stress of extra manual interaction. AI utilizes ML to ensure that cloud resources are available on demand and not constrained by sudden workload increases.

Utilizing AI to Optimize HCI Workloads

HCI is utilized for many different workloads. Some of the most demanding workloads include virtual desktop infrastructure (VDI), databases, and large commercial-off-the-shelf (COTS) software applications. The argument that AI will replace human jobs and human interaction is not such a bad thing in the case of these large workloads.

In fact, AI enhances and simplifies jobs held by humans, thus improving everyone’s experience with the software, from admins to end users. In the case of VDI, traffic generally fluctuates throughout the day. Classic examples include boot storms in the morning hours as people log in and pull down a desktop, and the end-of-day lull.

Leveraging AI within the HCI solution ensures resources are distributed based on the analytics gathered. When there’s a sudden increase of desktop demand due to an overnight maintenance activity, for instance, AI ensures the workload increase doesn’t stress the system and cause bottlenecks.

Find the Intersection and Put it to Use

There’s no need to fear the rise of AI and ML. Although science fiction movies depict AI as a replacement for the human race, this portrayal leads to a large misunderstanding of its true purpose. AI and ML are meant to automate activities that generally bog down administrative teams and require a lot of man hours.

AI is an analytics engine—when intersected with HCI, it can provide a powerful solution that will aid in optimizing workloads. Additionally, AI automates storage optimization and gives systems the capability to manage resources and predict workload behavior on their own.

When coupled together, HCI and AI provide a powerful solution that enables efficiency in the data center and enhances workload productivity. In the process of choosing an HCI solution, ensure that the vendor utilizes AI and ML—then you can embrace the latest and greatest technology to drive optimization and ease of use in your environment.


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