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Hyperconverged Infrastructure Part 1 - A Modern Infrastructure for Modern Manufacturing

Alan Conboy
Scale Computing

Hyperconvergence is a term that is gaining rapid interest across the manufacturing industry due to the undeniable benefits it has delivered to IT professionals seeking to modernize their data center, or as is a popular buzzword today ― "transform." Today, in particular, the manufacturing industry is looking to hyperconvergence for the potential benefits it can provide to its emerging and growing use of IoT and its growing need for edge computing systems.

In manufacturing today, IoT (Internet of Things) or commonly referred to as IIoT (industrial IoT) presents the opportunity to enjoy huge gains across industrial processes, supply chain optimization, and so much more ― providing the ability to create an "intelligent" factory, and a much smarter business. Edge computing and IoT enables manufacturing organizations to decentralize the workload, and to collect and process data at the edge or nearest to where the work is actually happening, which can overcome the "last mile" latency issues. In addition to reducing complexity and enabling easier collection and initial analyzing of data in real time.

Edge data centers can also be leveraged to offload processing work near end users, acting as an intermediary between the IoT edge devices and larger enterprises hosting the high-end compute resources, for more in-depth processing and analytics. However, many manufacturing organizations have faced a number of hurdles as they have endeavored to deploy, manage and enjoy the benefits of IoT and edge computing. And, that's where hyperconvergence can make all of the difference.

Unfortunately, the common misuse and misunderstanding of the term hyperconvergence has led to confusion and continues to act as a barrier for those that could otherwise benefit tremendously from an IT, business agility and profitability standpoint. Let's try to clear up that confusion here.

The Inverted Pyramid of Doom

Prior to hyperconverged infrastructure (and converged infrastructure), there was and still is the inverted pyramid of doom, which refers to a 3-2-1 model of system architecture. While it commonly got the job done in a few key areas, it is the polar opposite of what a business wants or needs today.

The 3-2-1 model consists of virtualization servers or virtual machines (VMs) running three or more clustered host servers, connected by two network switches, backed by a single storage device ― most commonly, a storage area network (SAN). The problem here is that the virtualization host depends completely on the network, which in turn depends completely on the single SAN. In other words, everything rests upon a single point of failure ― the SAN. (Of course, the false yet popular argument that the SAN can't fail because of dual controllers is a story for another time.)

Introducing Hyperconverged

When hyperconvergence was first introduced, it meant a converged infrastructure solution that natively included the hypervisor for virtualization. The "hyper" wasn't just hype as it is today. This is a critical distinction as it has specific implications for how architecture can be designed for greater storage simplicity and efficiency.

Who can provide a native hypervisor? Anyone can, really. Hypervisors have become a market commodity with very little feature difference between them. With free, open source hypervisors like KVM, anyone can build on KVM to create a hypervisor unique and specialized to the hardware they provide in their hyperconverged appliances. Many vendors still choose to stay with converged infrastructure models, perhaps banking on the market dominance of Vmware ― even with many consumers fleeing the high prices of VMware licensing.

Saving money is only one of the benefits of hyperconverged infrastructure. By utilizing a native hypervisor, the storage can be architected and embedded directly with the hypervisor, eliminating inefficient storage protocols, files systems, and VSAs. The most efficient data paths allow direct access between the VM and the storage; this has only been achieved when the hypervisor vendor is the same as the storage vendor. When the vendor owns the components, it can design the hypervisor and storage to directly interact, resulting in a huge increase in efficiency and performance.

In addition to storage efficiency, having the hypervisor included natively in the solution eliminates another vendor which increases management efficiency. A single vendor that provides the servers, storage, and hypervisor makes the overall solution much easier to support, update, patch, and manage without the traditional compatibility issues and vendor finger-pointing. Ease of management represents a significant savings in both time and training from the IT budget.

Our Old Friend, the Cloud

The cloud has been around for some time now, and most manufacturing organizations have leveraged it already, whether from an on-premises, remote or public cloud platform, or more commonly a combination of each (i.e. hybrid-cloud).

As a fully functional virtualization platform, hyperconverged infrastructure can nearly always be implemented alongside other infrastructure solutions as well as integrated with cloud computing. For example, with nested virtualization in cloud platforms, a hyperconverged infrastructure solution can be extended into the cloud for a unified management experience.

Not only does a hyperconverged infrastructure work alongside and integrated with cloud computing but it offers many of the benefits of cloud computing in terms of simplicity and ease-of-management on premises. In fact, for most organizations, a hyperconverged infrastructure may be the private cloud solution that is best suited to their environment.

Like cloud computing, a hyperconverged infrastructure is so simple to manage that it lets IT administrators focus on apps and workloads rather than managing infrastructure all day as is common in 3-2-1. A hyperconverged infrastructure is not only fast and easy to implement, but it can be scaled out quickly when needed. A hyperconverged infrastructure should definitely be considered along with cloud computing for data center modernization.

Read Hyperconverged Infrastructure Part 2 - What's Included, What's in It for Me and How to Get Started

Alan Conboy is the Office of the CTO at Scale Computing

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The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...

The gap is widening between what teams spend on observability tools and the value they receive amid surging data volumes and budget pressures, according to The Breaking Point for Observability Leaders, a report from Imply ...

Hyperconverged Infrastructure Part 1 - A Modern Infrastructure for Modern Manufacturing

Alan Conboy
Scale Computing

Hyperconvergence is a term that is gaining rapid interest across the manufacturing industry due to the undeniable benefits it has delivered to IT professionals seeking to modernize their data center, or as is a popular buzzword today ― "transform." Today, in particular, the manufacturing industry is looking to hyperconvergence for the potential benefits it can provide to its emerging and growing use of IoT and its growing need for edge computing systems.

In manufacturing today, IoT (Internet of Things) or commonly referred to as IIoT (industrial IoT) presents the opportunity to enjoy huge gains across industrial processes, supply chain optimization, and so much more ― providing the ability to create an "intelligent" factory, and a much smarter business. Edge computing and IoT enables manufacturing organizations to decentralize the workload, and to collect and process data at the edge or nearest to where the work is actually happening, which can overcome the "last mile" latency issues. In addition to reducing complexity and enabling easier collection and initial analyzing of data in real time.

Edge data centers can also be leveraged to offload processing work near end users, acting as an intermediary between the IoT edge devices and larger enterprises hosting the high-end compute resources, for more in-depth processing and analytics. However, many manufacturing organizations have faced a number of hurdles as they have endeavored to deploy, manage and enjoy the benefits of IoT and edge computing. And, that's where hyperconvergence can make all of the difference.

Unfortunately, the common misuse and misunderstanding of the term hyperconvergence has led to confusion and continues to act as a barrier for those that could otherwise benefit tremendously from an IT, business agility and profitability standpoint. Let's try to clear up that confusion here.

The Inverted Pyramid of Doom

Prior to hyperconverged infrastructure (and converged infrastructure), there was and still is the inverted pyramid of doom, which refers to a 3-2-1 model of system architecture. While it commonly got the job done in a few key areas, it is the polar opposite of what a business wants or needs today.

The 3-2-1 model consists of virtualization servers or virtual machines (VMs) running three or more clustered host servers, connected by two network switches, backed by a single storage device ― most commonly, a storage area network (SAN). The problem here is that the virtualization host depends completely on the network, which in turn depends completely on the single SAN. In other words, everything rests upon a single point of failure ― the SAN. (Of course, the false yet popular argument that the SAN can't fail because of dual controllers is a story for another time.)

Introducing Hyperconverged

When hyperconvergence was first introduced, it meant a converged infrastructure solution that natively included the hypervisor for virtualization. The "hyper" wasn't just hype as it is today. This is a critical distinction as it has specific implications for how architecture can be designed for greater storage simplicity and efficiency.

Who can provide a native hypervisor? Anyone can, really. Hypervisors have become a market commodity with very little feature difference between them. With free, open source hypervisors like KVM, anyone can build on KVM to create a hypervisor unique and specialized to the hardware they provide in their hyperconverged appliances. Many vendors still choose to stay with converged infrastructure models, perhaps banking on the market dominance of Vmware ― even with many consumers fleeing the high prices of VMware licensing.

Saving money is only one of the benefits of hyperconverged infrastructure. By utilizing a native hypervisor, the storage can be architected and embedded directly with the hypervisor, eliminating inefficient storage protocols, files systems, and VSAs. The most efficient data paths allow direct access between the VM and the storage; this has only been achieved when the hypervisor vendor is the same as the storage vendor. When the vendor owns the components, it can design the hypervisor and storage to directly interact, resulting in a huge increase in efficiency and performance.

In addition to storage efficiency, having the hypervisor included natively in the solution eliminates another vendor which increases management efficiency. A single vendor that provides the servers, storage, and hypervisor makes the overall solution much easier to support, update, patch, and manage without the traditional compatibility issues and vendor finger-pointing. Ease of management represents a significant savings in both time and training from the IT budget.

Our Old Friend, the Cloud

The cloud has been around for some time now, and most manufacturing organizations have leveraged it already, whether from an on-premises, remote or public cloud platform, or more commonly a combination of each (i.e. hybrid-cloud).

As a fully functional virtualization platform, hyperconverged infrastructure can nearly always be implemented alongside other infrastructure solutions as well as integrated with cloud computing. For example, with nested virtualization in cloud platforms, a hyperconverged infrastructure solution can be extended into the cloud for a unified management experience.

Not only does a hyperconverged infrastructure work alongside and integrated with cloud computing but it offers many of the benefits of cloud computing in terms of simplicity and ease-of-management on premises. In fact, for most organizations, a hyperconverged infrastructure may be the private cloud solution that is best suited to their environment.

Like cloud computing, a hyperconverged infrastructure is so simple to manage that it lets IT administrators focus on apps and workloads rather than managing infrastructure all day as is common in 3-2-1. A hyperconverged infrastructure is not only fast and easy to implement, but it can be scaled out quickly when needed. A hyperconverged infrastructure should definitely be considered along with cloud computing for data center modernization.

Read Hyperconverged Infrastructure Part 2 - What's Included, What's in It for Me and How to Get Started

Alan Conboy is the Office of the CTO at Scale Computing

Hot Topics

The Latest

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...

The gap is widening between what teams spend on observability tools and the value they receive amid surging data volumes and budget pressures, according to The Breaking Point for Observability Leaders, a report from Imply ...