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Why "Scaling Up" Your Network Infrastructure Always Leads to More Complexity and Cost - Part 1

Alastair Hartrup

IT departments are increasingly focused on major technological innovations that ultimately require frequent network infrastructure upgrades. Just look at the growing prominence of IoT, edge computing and software-defined networks. But according to Enterprise Management Associates, navigating the complexity of network architecture and attempting to scale or expand infrastructure are two of the top challenges for businesses when dealing with their networks. The problem: when networks aren't built to scale, emerging technologies end up out-pacing current network capacities, adding unnecessary cost and complexity.

Network Packet Brokers play a critical role in gaining visibility into these new complex networks. They deliver the packet data and information IT and security teams need to identify problems, recognize security issues, and ensure overall network performance. However, not all Packet Brokers are created equal when it comes to scalability. Simply "scaling up" your network infrastructure at every growth point is a more complex and more expensive endeavor over time — cutting into business profitability and productivity. Instead, building network architectures that can "scale out" — quickly adding ports, changing speeds or capabilities — is often a better approach.

Let's explore three ways the "scale up" approach to infrastructure growth impedes NetOps and security professionals (and the business as a whole). Based on these shortcomings, we can then dive into the benefits of scale-out visibility, which can help organizations grow when new technology initiatives alter network requirements.

1. Hardware Investments

When it comes to network infrastructure (including Packet Brokers), the "scale up" approach for hardware often includes buying a big box solution with a bazillion ports. Those ports get used as needed (which often translates to just a small fraction of them), while the unused ports sit idle for "future use" — a simple but wasteful growth solution. With networks growing at a faster rate than budgets these days, investing in idle-assets is often sub-optimal.

The other "scale up" approach is to purchase only the unit that matches today's exact needs, and then when required, decommission the existing unit and buy the next model up. Vendors like to promote the "product family" idea as scaling up. For example, if you purchase the X-1 now, you can later purchase the X-2, X-3, X-4 when you need more ports or power. This family scaling certainly can keep the customer loyal to a vendor by providing a simple upgrade path with familiar operation and management, but it's also wasteful as the smaller product is usually replaced well before the end of its useful life.

For many organizations, a better approach is to "scale out." Buy a smaller base unit that meets your immediate needs and build incrementally as you grow. This includes purchasing a right-sized base unit for initial requirements (call it the "mothership" appliance), then transparently adding on to the initial purchase as growth requires with modules that easily integrate (and leverage the intelligence of the "mothership" unit). This approach protects budget disciplined teams, while still providing a path for seamless (and less disruptive) growth in the future. The IT stakeholder no longer has to pay for something it might use in the future or purchase expensive new appliances along the way.

Read Why "Scaling Up" Your Network Infrastructure Always Leads to More Complexity and Cost - Part 2

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Performance bottlenecks aren't uncommon when it comes to rolling out new technology, regardless of how capable or game-changing that technology might be. Every generation of new tech has encountered roadblocks that had to be overcome before it was truly able to shine. Virtualization forced organizations to rethink resource allocation, cloud transformation had us shift our focus toward scalability and elasticity, and microservices introduced entirely new challenges around observability and distributed systems. There's something different about AI, however ...

Consider a single order represented across order-management, execution, and settlement systems. Each database, message broker, and application may be online and processing its own records correctly. Yet the workflow has failed if related events arrive on different clocks, rely on inconsistent state, or cannot be reconciled before an operational decision must be made ...

AI now exists in almost every IT workflow. In a recent survey of more than 800 IT service professionals, all respondents indicated the use of AI in some form within their organization. But there's a growing paradox: if dashboards are clearing faster and alerts are resolved at unprecedented speed, why aren't IT service desks reporting lighter workloads? The research found that 71% of IT teams said their actual workload has remained flat or increased since adopting AI. This reality appears to contradict what we’ve been told about AI ...

Two years ago, almost every customer conversation about AI started with the same questions: Which model should we use? What can it do? Is it ready for the enterprise? Today, those discussions have moved on. CIOs are far more interested in how to govern AI, integrate it with existing systems, prepare their workforce and make it part of everyday operations. The challenge is no longer to prove that AI can deliver value. It's instead about how to embed AI into the business in a way that's secure, scalable and delivers measurable outcomes ...

Two things happened to production incidents between 2023 and now, and they did not happen at the same speed. The first is that a class of dependency that barely existed three years ago now accounts for one incident in ten. Incidents disclosed by AI model and AI application providers rose from 1.7% of all disclosed unplanned incidents in 2023 to 10.7% in 2026 year to date, roughly a sixfold rise; that counts only incidents at AI companies themselves, so the true share is higher. The second is that the time to close an incident has not come down ...

Why "Scaling Up" Your Network Infrastructure Always Leads to More Complexity and Cost - Part 1

Alastair Hartrup

IT departments are increasingly focused on major technological innovations that ultimately require frequent network infrastructure upgrades. Just look at the growing prominence of IoT, edge computing and software-defined networks. But according to Enterprise Management Associates, navigating the complexity of network architecture and attempting to scale or expand infrastructure are two of the top challenges for businesses when dealing with their networks. The problem: when networks aren't built to scale, emerging technologies end up out-pacing current network capacities, adding unnecessary cost and complexity.

Network Packet Brokers play a critical role in gaining visibility into these new complex networks. They deliver the packet data and information IT and security teams need to identify problems, recognize security issues, and ensure overall network performance. However, not all Packet Brokers are created equal when it comes to scalability. Simply "scaling up" your network infrastructure at every growth point is a more complex and more expensive endeavor over time — cutting into business profitability and productivity. Instead, building network architectures that can "scale out" — quickly adding ports, changing speeds or capabilities — is often a better approach.

Let's explore three ways the "scale up" approach to infrastructure growth impedes NetOps and security professionals (and the business as a whole). Based on these shortcomings, we can then dive into the benefits of scale-out visibility, which can help organizations grow when new technology initiatives alter network requirements.

1. Hardware Investments

When it comes to network infrastructure (including Packet Brokers), the "scale up" approach for hardware often includes buying a big box solution with a bazillion ports. Those ports get used as needed (which often translates to just a small fraction of them), while the unused ports sit idle for "future use" — a simple but wasteful growth solution. With networks growing at a faster rate than budgets these days, investing in idle-assets is often sub-optimal.

The other "scale up" approach is to purchase only the unit that matches today's exact needs, and then when required, decommission the existing unit and buy the next model up. Vendors like to promote the "product family" idea as scaling up. For example, if you purchase the X-1 now, you can later purchase the X-2, X-3, X-4 when you need more ports or power. This family scaling certainly can keep the customer loyal to a vendor by providing a simple upgrade path with familiar operation and management, but it's also wasteful as the smaller product is usually replaced well before the end of its useful life.

For many organizations, a better approach is to "scale out." Buy a smaller base unit that meets your immediate needs and build incrementally as you grow. This includes purchasing a right-sized base unit for initial requirements (call it the "mothership" appliance), then transparently adding on to the initial purchase as growth requires with modules that easily integrate (and leverage the intelligence of the "mothership" unit). This approach protects budget disciplined teams, while still providing a path for seamless (and less disruptive) growth in the future. The IT stakeholder no longer has to pay for something it might use in the future or purchase expensive new appliances along the way.

Read Why "Scaling Up" Your Network Infrastructure Always Leads to More Complexity and Cost - Part 2

Hot Topics

The Latest

Performance bottlenecks aren't uncommon when it comes to rolling out new technology, regardless of how capable or game-changing that technology might be. Every generation of new tech has encountered roadblocks that had to be overcome before it was truly able to shine. Virtualization forced organizations to rethink resource allocation, cloud transformation had us shift our focus toward scalability and elasticity, and microservices introduced entirely new challenges around observability and distributed systems. There's something different about AI, however ...

Consider a single order represented across order-management, execution, and settlement systems. Each database, message broker, and application may be online and processing its own records correctly. Yet the workflow has failed if related events arrive on different clocks, rely on inconsistent state, or cannot be reconciled before an operational decision must be made ...

AI now exists in almost every IT workflow. In a recent survey of more than 800 IT service professionals, all respondents indicated the use of AI in some form within their organization. But there's a growing paradox: if dashboards are clearing faster and alerts are resolved at unprecedented speed, why aren't IT service desks reporting lighter workloads? The research found that 71% of IT teams said their actual workload has remained flat or increased since adopting AI. This reality appears to contradict what we’ve been told about AI ...

Two years ago, almost every customer conversation about AI started with the same questions: Which model should we use? What can it do? Is it ready for the enterprise? Today, those discussions have moved on. CIOs are far more interested in how to govern AI, integrate it with existing systems, prepare their workforce and make it part of everyday operations. The challenge is no longer to prove that AI can deliver value. It's instead about how to embed AI into the business in a way that's secure, scalable and delivers measurable outcomes ...

Two things happened to production incidents between 2023 and now, and they did not happen at the same speed. The first is that a class of dependency that barely existed three years ago now accounts for one incident in ten. Incidents disclosed by AI model and AI application providers rose from 1.7% of all disclosed unplanned incidents in 2023 to 10.7% in 2026 year to date, roughly a sixfold rise; that counts only incidents at AI companies themselves, so the true share is higher. The second is that the time to close an incident has not come down ...