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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

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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 ...