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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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

Related Links

Hot Topics

The Latest

When the Database Is the Problem, Is APM Enough?

Production incidents rarely announce themselves as database problems. They appear as slow transactions, timeouts, rising response times, or an application struggling under a workload it previously handled. APM provides an essential starting point. It can identify a slow transaction path, highlight an affected service, and show that a database dependency is consuming more time than expected. But identifying the database as part of the problem is not the same as explaining what is happening inside it ...

From Cleanup to Prevention: The Future of Cloud-Native Cost Optimization

Cloud teams are under constant pressure to reduce spend without slowing development or increasing operational risk. They are deploying autoscalers, rightsizing workloads, enforcing resource requests, reviewing utilization dashboards, and building FinOps processes around cloud-native environments. Yet the results often disappoint ...

The Real Enterprise AI Risk Isn't Hallucinations

Ask most IT leaders about their biggest concern with AI and you'll hear the same answer: hallucinations ... Today, however, the conversation has shifted ... As organizations move beyond chatbots and experiments, they are increasingly deploying AI agents that perform multi-step tasks. These systems retrieve documents, query databases, call APIs, generate reports, write code, and make recommendations. The issue is not whether the model can reason. The issue is whether the organization can see, verify, and govern the decisions being made along the way ...

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While organizations want to take control of their telemetry, building telemetry pipelines from scratch can be a very daunting, complicated task, even when leveraging open-source standards like OpenTelemetry. It requires specialized knowledge across distributed systems, data engineering, and security. This fragmented approach across systems causes higher operational costs; it puts a strain on resources and reduces efficiency as teams have to work with different interfaces and processes ...

The Corporate Network Is Fading - Here's What Replaces It

For decades, enterprise networks were designed around a simple assumption: work happened inside the office. Applications lived in centralized data centers, employees connected through internal infrastructure, and security focused on protecting the perimeter that surrounded everything ... But the way organizations operate today bears little resemblance to that environment. Cloud platforms host critical applications, employees connect from homes and airports as often as they do from offices, and partners collaborate through shared systems that exist far beyond corporate walls. In short, the corporate network no longer resembles the environment it was designed to protect ...

IT Leaders Must Establish a Network Data Architecture Practice

As an analyst who researches how IT organizations design, build, and operate their networks, I find that network data is a constant source of pain. Network teams struggle with data quality, fragmentation, authority, access, and trust. And these issues undermine everything they try to do. Here are the numbers: Only 45% of network teams are completely confident in the accuracy of their network source of truth, which documents the intent of their network ...

Data Center Outages Are Decreasing but More Costly

The 2026 Global Data Center Survey from Uptime Institute reveals an industry navigating workforce constraints, escalating outage expenses, even as rising costs remain the top concern for management teams ...

Observability Has a Physical Layer Now: What AI Infrastructure Teaches IT Operations

The next observability gap may not be in the code. It may be under the rack. That sounds strange until you think about how AI incidents actually feel in the middle of an investigation ... The application dashboard may be accurate. It may also be stopping at the wrong boundary. AI systems depend on software, but they also depend on a dense physical stack: racks, power paths, thermal margin, maintenance activity and, in many environments, liquid cooling. Those physical dependencies can change slowly before they look like a software incident ...

TLS Certificate Expiration Is Becoming an Observability Problem

Certificate expiration is the rare outage you can see coming. Every TLS certificate carries the date it stops working, so the moment it will begin breaking connections is knowable in advance. That's what makes an expired certificate such a frustrating way to lose a service. What's changing now is how often that date comes around ...

Enterprise Cloud Observability: Choosing the Right Starting Point

Enterprises operate different combinations of workloads across cloud, hybrid and multicloud environments. For business-critical workloads, teams need to consider monitoring and observability early so they can detect health issues, investigate failures, and understand operational impact. Organizations place workloads on cloud platforms based on a combination of technical requirements, economics, existing dependencies, organizational standards, and business priorities. Their monitoring priorities therefore depend on what they operate and where those systems run. Those priorities will not look the same for every organization ...