Editor’s Note: This is the 62nd article in the Real Words or Buzzwords? magazine series on how real words become empty words and stifle technological advances.
“Autonomous computing infrastructure” refers to an IT infrastructure (computing, network, and data storage) that manages itself throughout its lifecycle with minimal or no human intervention.
This is the current state of information technology development and is of great importance for organizations using electronic physical security systems, especially for organizations with critical infrastructures.
I will be speaking about this at the Converge Security Conference on Friday, September 30, 2022 in my 4:00 p.m. session entitled: “Lessons from the Frontlines of Security Convergence” at the Marriott Hotel near the Anaheim, California Convention Center.
This and related topics are also part of the 3-5 year look ahead to the future of security leadership and physical security technology at the Global Security Operations event (GSO 2025) on November 2-3, 2022 at Vari (formerly VariDesk ) Headquarters in Irving, Texas.
This article is about automating the management of the computing infrastructure itself, not automating applications that reside on it. This is a separate topic and gets its own article.
The growth of compute virtualization
For over a decade, both cloud data centers and on-premises data centers have utilized virtualized computing environments whose management software takes any number of separate hardware resources (servers, networks, and data storage) and aggregates them to create virtual pools of resources – virtual servers, virtual networks, and virtual memory.
The virtualized resource pools are much easier to manage than individual pieces of hardware. As explained in this TechTarget article, the approach eventually became known as the software-defined data center (SDDC), a term coined around 2012 by former VMware CTO Steve Herrod.
The resulting data center explosion
Over the following 10 years, continuous advances in computer hardware and virtualization software have given us the ability to create very large pools of virtual CPUs and GPUs, virtual RAM, as well as software-defined networking (SDN) and software-defined storage (SDS) all connectable at gigabit speeds.
These can be assembled as large clusters of connected, high-performance virtualization appliances that provide highly scalable pools for any type of computing resource.
The advancement of software to automate and orchestrate the complex configuration and management of the vast amount of virtualized hardware resources has made it possible to manage the elements of large virtual systems from a single pane of glass (the same computer screen).
These ongoing advances in computing have helped fuel the explosive growth of data centers required to support the ever-increasing number of software applications and systems of ever larger and more complex sizes.
However, despite advances in data center automation and orchestration, data center staff remain overworked, exponentially increasing the opportunities for human error, system failures, and the resulting security vulnerabilities.
Fortunately, these same computing advances have enabled the development and use of artificial intelligence software capable of managing large volumes of virtualized IT infrastructure with minimal IT staff involvement, eliminating traditional staff errors, and reducing deployment times to a fraction reduce what they used to be.
Autonomous computing infrastructure
In the IT world, autonomous computing infrastructure is the equivalent of a self-driving vehicle. It is the intelligent (i.e. AI-based) automation of IT infrastructure management workflow that used to require highly skilled and specialized IT staff.
“Autonomous computing infrastructure is a continuum,” says Dell Technologies in its highly insightful position paper, which introduced the concept in February 2021 and further explained six months later in a whitepaper titled. “An introduction to Dell Technologies’ Autonomous Framework.”
This white paper describes a six-tiered autonomous framework for IT operations, from no automation to full autonomy. It also provides background information, goes into each level of this framework, and provides some reflections for moving forward.
Autonomous infrastructure self-government
Basically, autonomous computing infrastructure refers to the degree of self-governance of an IT infrastructure deployment throughout its lifecycle.
This goes far beyond redundancy-based fault tolerance, self-healing networks, etc.
Examples include the ability to expand a security system’s deployment by adding new compute, storage, networking, or security device hardware and automatically expanding existing pools of virtual resources and reallocating existing applications without incurring system downtime or system performance Degradation.
This allows the autonomous computing infrastructure to sustain itself at 99.9999% (“six nines”) uptime—allowing for a total of 31 seconds of downtime per year.
Such infrastructure self-management could include automatically copying a virtual machine running security applications from one physical server to another in order to achieve higher performance from a newly added server in the resource pool without disrupting the application.
This type of change would be based on AI predictive analysis of system performance and application load trends.
The automation capabilities would also report projected future needs for system expansion or redistribution options for newly developed spare capacity. Determining the scope of self-management can be performed by a single IT person instead of the previously required team of IT specialists.
Central change in IT infrastructure management
I believe, as Dell says, “We are on the cusp of a radically new approach to technology, powered by Autonomous Operations (AO), which allows organizations to radically delegate all kinds of simple, repetitive, and non-strategic IT tasks intelligent technology.”
Since we are all familiar with automobiles, Dell was able to leverage our understanding by comparing the autonomous computing spectrum to the different levels of autonomous driving.
Radical new technological approaches
Dell very accurately makes another analogy in relation to the automotive industry.
Jon Siegal, senior vice president, product marketing, Dell Technologies, wrote, “The invention of the internal combustion engine changed society forever. At the time of its inception, most people were unaware that history was in the making. That it would create a hypermobile society that would open the floodgates to all kinds of cars, trains and planes. That it would pave the way for quick trade and thus more prosperity. That it would revolutionize farming and change farming, leading to more food at lower prices. And that it would remove the limits on how far we could travel through physical exertion.”
Siegal continues, “Similarly, corporate leaders today may not realize that they are presiding over such a pivotal moment in time. We are on the cusp of a radical new approach to technology powered by Autonomous Operations (AO) that will enable companies to finally “take control by letting go in reverse”.
With AO, organizations can radically delegate all kinds of simple, repetitive, and non-strategic IT tasks to intelligent technology while experiencing the thrill of reaching new heights by pushing boundaries that were previously unattainable. By partnering more fully with machines, companies can also improve employee productivity and satisfaction, and ultimately the customer experience.”
I encourage you to read the rest of Siegal’s article, Making History with Autonomous Operations, because while the discussion revolves around the far-reaching business implications of autonomous computing infrastructure, much of the article is more electronic for our reflections on current and near-future states physical security systems.
Use of the Autonomous Compute Infrastructure Framework
There are many ways for physical security practitioners to leverage Dell’s autonomous computing infrastructure framework, such as: B. Assessing one’s current infrastructure, evaluating vendors’ product roadmaps, and prioritizing certain elements and capabilities of security systems when planning upgrades.
More broadly, it provides a cross-domain perspective for digital transformation stakeholders and a touchpoint for discussions about physical security’s involvement in the organization’s digital transformation efforts and initiatives.
Ray Bernard, PSP CHS-III, is the principal consultant of Ray Bernard Consulting Services (RBCS), a company providing security consulting services to public and private entities (www.go-rbcs.com). In 2018, IFSEC Global ranked Ray 12th in the top 30 global security thought leaders. He is the author of the Elsevier book Insights into the convergence of security technology available on Amazon. Follow Ray on Twitter: @RayBernardRBCS.