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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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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

While companies adopt AI at a record pace, they also face the challenge of finding a smart and scalable way to manage its rapidly growing costs. This requires balancing the massive possibilities inherent in AI with the need to control cloud costs, aim for long-term profitability and optimize spending ...

Telecommunications is expanding at an unprecedented pace ... But progress brings complexity. As WanAware's 2025 Telecom Observability Benchmark Report reveals, many operators are discovering that modernization requires more than physical build outs and CapEx — it also demands the tools and insights to manage, secure, and optimize this fast-growing infrastructure in real time ...

As businesses increasingly rely on high-performance applications to deliver seamless user experiences, the demand for fast, reliable, and scalable data storage systems has never been greater. Redis — an open-source, in-memory data structure store — has emerged as a popular choice for use cases ranging from caching to real-time analytics. But with great performance comes the need for vigilant monitoring ...

Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...

AI is the catalyst for significant investment in data teams as enterprises require higher-quality data to power their AI applications, according to the State of Analytics Engineering Report from dbt Labs ...

Misaligned architecture can lead to business consequences, with 93% of respondents reporting negative outcomes such as service disruptions, high operational costs and security challenges ...

A Gartner analyst recently suggested that GenAI tools could create 25% time savings for network operational teams. Where might these time savings come from? How are GenAI tools helping NetOps teams today, and what other tasks might they take on in the future as models continue improving? In general, these savings come from automating or streamlining manual NetOps tasks ...

IT and line-of-business teams are increasingly aligned in their efforts to close the data gap and drive greater collaboration to alleviate IT bottlenecks and offload growing demands on IT teams, according to The 2025 Automation Benchmark Report: Insights from IT Leaders on Enterprise Automation & the Future of AI-Driven Businesses from Jitterbit ...

A large majority (86%) of data management and AI decision makers cite protecting data privacy as a top concern, with 76% of respondents citing ROI on data privacy and AI initiatives across their organization, according to a new Harris Poll from Collibra ...

According to Gartner, Inc. the following six trends will shape the future of cloud over the next four years, ultimately resulting in new ways of working that are digital in nature and transformative in impact ...