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Ease IoT Complexity at the Branch with SD-WAN

Shamus McGillicuddy

Software-defined WAN (SD-WAN) solutions will play an important role in enabling enterprise Internet of Things (IoT) initiatives. Most enterprises are experiencing growth in the number of endpoints connecting to the network in branch offices and remote sites. According to recent Enterprise Management Associates (EMA) research, 59% of enterprises say that IoT devices are a significant source of that growth, contributing more than smartphones and tablets.

IoT devices in remote sites will present challenges around bandwidth, security, policy management, and more. These challenges will be exacerbated by the fact that most remote sites lack onsite IT personnel who can ensure that IoT devices are safely connected to the network. SD-WAN solutions have the potential to address many of these challenges.

SD-WAN Addresses IoT Bandwidth Demands, QoS Complexity

Nearly half (47%) of network professionals involved in an IoT initiative are delivering IoT connectivity via their existing WAN services, according to EMA research. Additionally, 77% of network teams expect their IoT ecosystems to add “significant” or “very significant” traffic to the network by next year. And many IoT applications receive a high priority on the network.

Our research has found that 27% of network pros assign IoT traffic a high quality of service (QoS) setting, and only 12% dump IoT traffic into a best-effort QoS tier. Thus, IoT devices not only demand bandwidth. They may bump other applications out of the way. Enterprises are going to need more bandwidth and more sophisticated QoS technology.

Fortunately, SD-WAN solutions deliver added bandwidth and granular, easy-to-use QoS controls. SD-WAN solutions are highly programmable via a graphical user interface, allowing engineers to assign and adjust QoS settings for applications on a global basis. SD-WAN solutions also enable hybrid connectivity, allowing enterprises to supplement MPLS connectivity with cheaper broadband links. This drives down the cost per bit at remote sites without sacrificing network service reliability.

SD-WAN Will Help Secure IoT at the Branch

The very same programmatic controls that facilitate QoS management in SD-WAN technology will also help enterprises segment the network and secure IoT connectivity. From a central location, network engineers can use a SD-WAN’s graphical user interface to create global VLANs dedicated to IoT, ensuring that thermostats and factory robots are never able to ping a financial records database, for instance. This is critical because IoT presents myriad security problems for the network team.

IoT devices often lack the power and computing resources necessary to support channel-based security techniques like TLS. These same constraints also make it difficult for network managers to discover IoT devices with traditional network management tools, which challenges their ability to find rogue devices. Strict LAN segmentation that maps onto SD-WAN segments ensures that isolation is maintained throughout the network.

SD-WAN solutions also typically offer native or third-party integrated security services, like firewall and malware detection. These security services can be deployed in a distributed architecture, allowing granular security controls at every branch. As EMA research has shown that nearly half of network teams (47%) have determined that their existing network security infrastructure cannot cope with the scale of their IoT ecosystems, these security controls will prove especially valuable to enterprises. The distributed security model of an SD-WAN solution will ease this scaling issue.

If you are supporting IoT on your WAN, ask your SD-WAN vendor if they can help.

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Ease IoT Complexity at the Branch with SD-WAN

Shamus McGillicuddy

Software-defined WAN (SD-WAN) solutions will play an important role in enabling enterprise Internet of Things (IoT) initiatives. Most enterprises are experiencing growth in the number of endpoints connecting to the network in branch offices and remote sites. According to recent Enterprise Management Associates (EMA) research, 59% of enterprises say that IoT devices are a significant source of that growth, contributing more than smartphones and tablets.

IoT devices in remote sites will present challenges around bandwidth, security, policy management, and more. These challenges will be exacerbated by the fact that most remote sites lack onsite IT personnel who can ensure that IoT devices are safely connected to the network. SD-WAN solutions have the potential to address many of these challenges.

SD-WAN Addresses IoT Bandwidth Demands, QoS Complexity

Nearly half (47%) of network professionals involved in an IoT initiative are delivering IoT connectivity via their existing WAN services, according to EMA research. Additionally, 77% of network teams expect their IoT ecosystems to add “significant” or “very significant” traffic to the network by next year. And many IoT applications receive a high priority on the network.

Our research has found that 27% of network pros assign IoT traffic a high quality of service (QoS) setting, and only 12% dump IoT traffic into a best-effort QoS tier. Thus, IoT devices not only demand bandwidth. They may bump other applications out of the way. Enterprises are going to need more bandwidth and more sophisticated QoS technology.

Fortunately, SD-WAN solutions deliver added bandwidth and granular, easy-to-use QoS controls. SD-WAN solutions are highly programmable via a graphical user interface, allowing engineers to assign and adjust QoS settings for applications on a global basis. SD-WAN solutions also enable hybrid connectivity, allowing enterprises to supplement MPLS connectivity with cheaper broadband links. This drives down the cost per bit at remote sites without sacrificing network service reliability.

SD-WAN Will Help Secure IoT at the Branch

The very same programmatic controls that facilitate QoS management in SD-WAN technology will also help enterprises segment the network and secure IoT connectivity. From a central location, network engineers can use a SD-WAN’s graphical user interface to create global VLANs dedicated to IoT, ensuring that thermostats and factory robots are never able to ping a financial records database, for instance. This is critical because IoT presents myriad security problems for the network team.

IoT devices often lack the power and computing resources necessary to support channel-based security techniques like TLS. These same constraints also make it difficult for network managers to discover IoT devices with traditional network management tools, which challenges their ability to find rogue devices. Strict LAN segmentation that maps onto SD-WAN segments ensures that isolation is maintained throughout the network.

SD-WAN solutions also typically offer native or third-party integrated security services, like firewall and malware detection. These security services can be deployed in a distributed architecture, allowing granular security controls at every branch. As EMA research has shown that nearly half of network teams (47%) have determined that their existing network security infrastructure cannot cope with the scale of their IoT ecosystems, these security controls will prove especially valuable to enterprises. The distributed security model of an SD-WAN solution will ease this scaling issue.

If you are supporting IoT on your WAN, ask your SD-WAN vendor if they can help.

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The Latest

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 ...

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Many organizations assumed their infrastructure strategy was settled. It had been implemented, optimized and built into long-term plans. Recent changes in technology and vendor consolidation are forcing a second look. Cloud outages and licensing changes have exposed how much dependency exists on a small number of platforms. As a result, organizations are reevaluating whether those decisions still hold up under current conditions ...

Edge AI is strategically embedded in core IT and infrastructure spending across industries, according to the 2026 Edge AI Survey from ZEDEDA. The research shows that 83% of C-suite and IT executive respondents say edge AI is important to their core business strategy ...

As AI adoption accelerates, operational complexity — not model intelligence — is becoming the primary barrier to reliable AI at scale, according to the State of AI Engineering 2026 from Datadog ... The report highlights a compounding complexity challenge as AI systems scale ... Around 5% of AI model requests fail in production, with nearly 60% of those failures caused by capacity limits ...

For years, production operations teams have treated alert fatigue as a quality-of-life problem: something that makes on-call rotations miserable but isn't considered a direct contributor to outages. That framing doesn't capture how these systems fail, and we now have data to show why. More importantly, it's now clear alert fatigue is a symptom of a deeper issue: production systems have outgrown the current operational approaches ...