Self-driving Data Center

As workloads of enterprises become more sophisticated, so do the IT infrastructures which support them. Most companies implement technology such as automation, software-defined tools, and AI to help alleviate the burden. These not only simplify operations and enhance resource utilization but also provide the basis for new technology development in IT administration: the self-driving data center.

The self-driving data center needs no human intervention to manage everyday operations. Tools supporting the data center work together to automate and manage infrastructure intelligently, while optimizing operations and proactively addressing potential problems. However, the self-driving data center at this point is more of a general objective than a reality. That being said, with vendors such as VMware with vRealize AI (formerly Project Magma) and Hewlett Packard Enterprise with Synergy and Composable Rack and IT technology developments paving the way, don’t be surprised if you see real, not-too-distant, self-driven data center capabilities.

Advantages of a self-driving data center

A self-driving data center may offer a variety of benefits, among the most significant being efficiency. It automates and streamlines IT resources-requiring processes, removing repetitive manual activities, reducing service delays, and saving time and money. Since the self-driving network integrates intelligence, the data center benefits from real-time analytics contributing to actionable insights and quicker results. Simultaneously, automation technologies and software-defined capabilities make for more efficient management and faster problem-solving.

This productivity frees IT, workers, for further efforts as well. They will focus on learning new technology, gaining more advanced skills, and designing cutting-edge products rather than wasting their days making hundreds of decisions and performing repetitive tasks. IT professionals spend less time providing, upgrading, putting out fires, carrying out other tasks, and moving an organization forward to deliver a competitive edge.

Another advantage of the self-driven data center’s software-defined capabilities is a reduction in the types of errors that can be caused by repetitive manual tasks. When processes are automated and backed by intelligent systems, operational precision becomes the norm. The more monotonous and manual the operation, the more significant will be the chance of making errors, particularly in fluctuating environments.

The principle for the self-driving data center

IT personnel perform most of the operations manually in the traditional data center, be it general maintenance, troubleshooting problems, or problem-solving. But the more complex a data center grows, the harder it becomes to keep equipment and applications running smoothly. Virtualization helps to some degree, by allowing efficient use of resources, but it also requires a reasonable amount of manual effort. Some have turned to frameworks such as converged or hyper-converged infrastructures to address these challenges. These can help unravel and streamline operations, but they still require a great deal of handholding, especially for the DIY. For example, administrators who handle hyper-converged workloads that often have to resolve unexpected issues of contention or delays in service.

However, one thing hyper-convergence has done is to mobilize the forces behind the software-defined infrastructure (SDI) concept, which puts IT systems under software control, helping to simplify operations and allow better use of resources. The SDI approach applied mainly to computing and storing resources in the early days of hyper-converged infrastructure, but today’s hyper-converged systems also include software-defined networking to develop a more comprehensive software-defined data center.

With SDI, a system becomes more manageable, but it also lends itself to automation, supporting methodologies of development like infrastructure as code. Together with SDI, automation simplifies processes such as resource provisioning, updating, monitoring components, balancing allocations, taking corrective action, and performing a variety of other tasks, without (or, at least, very little) manual input needed.

But automation is just as efficient as the controls that are in place to ensure that operations work as planned or, better still, keep improving. AI-based intelligence enters the picture here. Today’s intelligent systems integrate AI technologies such as machine learning and deep learning, as well as predictive analytics, to fully utilize an automated SDI’s capabilities.

The AI-based service collects, aggregates, and analyzes the accumulated data from system metrics across monitored components. It uses these findings to determine what operations to perform and how best to do so. Intelligent services are not stopping there. As data continues to accumulate, and the world gets better understood, they keep rising smarter.

Switching towards the self-driving data center

So, infrastructures become more standardized, smarter, and software-defined with each passing day. As systems become more abundant and sophisticated, we are moving closer to the exact data center which drives itself. Self-driving capabilities are now a reality in IT infrastructures. As systems become more streamlined and more software-driven, we will continue to move closer to the self-driving data center goal. Self-diagnostic and self-healing IT infrastructures would help streamline operations, reduce human error, and free up IT resources to concentrate on more innovative opportunities.


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