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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

Hot Topics

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I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...

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

I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...