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What is SDN?

Early Adopters Define Sofware-Defined Networking
Shamus McGillicuddy

Greg Ferro recently blogged about how attempts to define software-defined networking (SDN) are a waste of time. He wrote: "You can’t define 'Software Defined Network' because it's not a thing. It's not a single thing or even a few things. It's combination of many things including intangibles. Stop trying to define it. Just deploy it."

To a great extent I agree with him. It’s hard to define SDN as one thing, given that it is applied to so many different areas of networking: Data centers, enterprise campus, the WAN, radio access networks, etc. And each vendor that introduces an SDN product to the market is working from a definition that fits into its own strategy. Cisco’s is hardware-centric. VMware’s is software-centric, and so on.

So, yes. Just deploy it. But … what do those people who deploy SDN have to say?

EMA did offer a definition of SDN in its recently published research report Managing Tomorrow’s Networks: The Impacts of SDN and Network Virtualization on Network Management. The research is based on a survey of 150 enterprises that have deployed SDN in production or have plans to do so within 12 months. The report explores the benefits and challenges of SDN. Much of the research explores the readiness of incumbent network management tools to support SDN infrastructure and it identifies new functional requirements for these management tools.

(Side note: We also surveyed 76 communications service providers on the same topics, but I’m limiting this blog discussion to enterprise networking).

Since we were surveying people who were actually implementing SDN, we thought it would be valuable to get their take on what SDN actually is. We asked them the following question: When thinking about the definition of SDN, what characteristics of an SDN solution are important to you? Here are the top three defining characteristics of SDN for early enterprise adopters:

■ Centralized controller (39% of respondents)

■ Fluid network architecture (27%)

■ Low-cost hardware (25%)

A decoupled control plane and data plane (13%) was tied with intent-based networking as the least important defining aspect of SDN solutions.

These top three responses from early adopters of SDN present a pretty simple definition of the technology. And when you think about it, these terms align what we’re seeing in the market place. Nearly every SDN solution has a centralized controller, or at least a centrally accessible, distributed controller. This controller serves as a single point of control, access, programmability and data collection for the network. Most solutions also offer low-cost hardware, or — in the case of overlays — require no new hardware.

Fluid network architecture, I would argue, gets to the heart of what SDN is all about. It enables networks that are flexible and responsive to changes in infrastructure conditions and business requirements. This contrasts sharply with static, highly manual legacy networks, where any change to network connectivity in a data center or a remote site can require days, weeks or even months to implement. SDN’s promise is a network that can respond to change quickly and fluidly, thanks to increased programmability, for instance.

Therefore, I defer to the wisdom of early adopters when trying to come with up a definition. SDN is characterized by a fluid network architecture that is enabled by a centralized controller and low-cost hardware.

One final point on the subject of defining SDN. We asked early adopters of software-defined WAN (SD-WAN) a similar but distinct question on the defining characteristics of SD-WAN, which EMA considers sufficiently different from other varieties of SDN to warrant its own definition. In the case of SD-WAN, cloud-based network and security services were the number one defining aspect of such solutions. Centralized control was the number two priority, followed by hybrid WAN connectivity.

Shamus McGillicuddy is Senior Analyst, Network Management at Enterprise Management Associates (EMA).

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What is SDN?

Early Adopters Define Sofware-Defined Networking
Shamus McGillicuddy

Greg Ferro recently blogged about how attempts to define software-defined networking (SDN) are a waste of time. He wrote: "You can’t define 'Software Defined Network' because it's not a thing. It's not a single thing or even a few things. It's combination of many things including intangibles. Stop trying to define it. Just deploy it."

To a great extent I agree with him. It’s hard to define SDN as one thing, given that it is applied to so many different areas of networking: Data centers, enterprise campus, the WAN, radio access networks, etc. And each vendor that introduces an SDN product to the market is working from a definition that fits into its own strategy. Cisco’s is hardware-centric. VMware’s is software-centric, and so on.

So, yes. Just deploy it. But … what do those people who deploy SDN have to say?

EMA did offer a definition of SDN in its recently published research report Managing Tomorrow’s Networks: The Impacts of SDN and Network Virtualization on Network Management. The research is based on a survey of 150 enterprises that have deployed SDN in production or have plans to do so within 12 months. The report explores the benefits and challenges of SDN. Much of the research explores the readiness of incumbent network management tools to support SDN infrastructure and it identifies new functional requirements for these management tools.

(Side note: We also surveyed 76 communications service providers on the same topics, but I’m limiting this blog discussion to enterprise networking).

Since we were surveying people who were actually implementing SDN, we thought it would be valuable to get their take on what SDN actually is. We asked them the following question: When thinking about the definition of SDN, what characteristics of an SDN solution are important to you? Here are the top three defining characteristics of SDN for early enterprise adopters:

■ Centralized controller (39% of respondents)

■ Fluid network architecture (27%)

■ Low-cost hardware (25%)

A decoupled control plane and data plane (13%) was tied with intent-based networking as the least important defining aspect of SDN solutions.

These top three responses from early adopters of SDN present a pretty simple definition of the technology. And when you think about it, these terms align what we’re seeing in the market place. Nearly every SDN solution has a centralized controller, or at least a centrally accessible, distributed controller. This controller serves as a single point of control, access, programmability and data collection for the network. Most solutions also offer low-cost hardware, or — in the case of overlays — require no new hardware.

Fluid network architecture, I would argue, gets to the heart of what SDN is all about. It enables networks that are flexible and responsive to changes in infrastructure conditions and business requirements. This contrasts sharply with static, highly manual legacy networks, where any change to network connectivity in a data center or a remote site can require days, weeks or even months to implement. SDN’s promise is a network that can respond to change quickly and fluidly, thanks to increased programmability, for instance.

Therefore, I defer to the wisdom of early adopters when trying to come with up a definition. SDN is characterized by a fluid network architecture that is enabled by a centralized controller and low-cost hardware.

One final point on the subject of defining SDN. We asked early adopters of software-defined WAN (SD-WAN) a similar but distinct question on the defining characteristics of SD-WAN, which EMA considers sufficiently different from other varieties of SDN to warrant its own definition. In the case of SD-WAN, cloud-based network and security services were the number one defining aspect of such solutions. Centralized control was the number two priority, followed by hybrid WAN connectivity.

Shamus McGillicuddy is Senior Analyst, Network Management at Enterprise Management Associates (EMA).

Hot Topics

The Latest

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 ...

Seamless shopping is a basic demand of today's boundaryless consumer — one with little patience for friction, limited tolerance for disconnected experiences and minimal hesitation in switching brands. Customers expect intuitive, highly personalized experiences and the ability to move effortlessly across physical and digital channels within the same journey. Failure to deliver can cost dearly ...

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