4 Strategies for Ensuring Application Performance Across Hybrid Networks

Jay Botelho

As network transformation initiatives like SD-WAN, edge computing and public/private clouds are adopted at increasing rates, hybrid networks are quickly becoming the new normal for IT and NetOps professionals. According to a recent survey from Sirkin Research, 35% of enterprise networking and IT professionals struggle to access visibility into performance across all fabrics of the network. Without visibility into these hybrid network environments, NetOps are unable to troubleshoot the business-critical applications every organization relies on today.

Here are four ways IT and NetOps teams can gain better visibility into complex, hybrid networks:

Wireless Packet Capture

As personal devices and IoT becomes more common in the workplace, wireless issues are only going to increase. If you can't track performance across the entire end-to-end network, then you can't truly ensure end-user performance. Monitoring all wireless traffic isn't realistic for most organizations because it requires too many capture points spread throughout the wireless network to be viable.

A better approach is to supplement flow data and packet data from wired network segments with ad-hoc wireless packet capture for issues that can't be resolved based on the flow data alone. Sending a network engineer on-site to conduct a packet capture is one option, but it's extremely expensive. It's possible, with the right setup, to use a nearby AP as a sensor to sniff wireless traffic between a client and an access point for a short time. This isn't a common capability today, but I believe organizations need to start designing this into their networks to gain better visibility into the wireless network, and a better understanding of the hybrid network overall.

Packet Analysis for Complex Issues

There's a common adage in networking that says 80% of issues can be resolved using flow data and that the 20% that can't will require packet data. Why? Because many of these more complex problems have tons of potential causes.

For example, an end user complains that an application is running slowly. Maybe it's the network or possibly a QoS issue with the new SD-WAN that's been being rolled out. But the application could also be at fault; perhaps it wasn't perfectly designed and it's letting multiple users try to change an element of its database simultaneously, resulting in longer processing times. Without quick access to packet data, these difficult application issues can't be resolved successfully.

There are several free packet capture and analysis tools available, but larger organizations with complex networks will likely need to invest in a packet capture and analysis product that offers features like network mapping, customizable, pre-built reports, and visualizations to simplify this process and speed up troubleshooting.

Deep Packet Inspection as a Supplement to Flow Data

NetFlow and similar types of network telemetry data all have limitations. For example, when using NetFlow or IPFIX to troubleshoot VoIP calls, this data includes IP addresses, but not phone numbers. Customers calling to complain about VoIP will know their number, but probably not their IP address, so IT has no way to looking up the flows they need to hunt down the problem! With additional applications that offer deep packet inspection (DPI) it's possible to "add" new data points, such as the phone number of a VoIP call, to flow data. TCP retries is another useful data point that could be added. By adding selective data points to NetFlow, flow-based monitoring tools become much more useful for new situations that hybrid networks create.

Gather Data Necessary for SD-WAN Planning, Verification and Management

When transitioning to SD-WAN, enterprises need visibility into their existing network devices to determine the baseline of existing application performance, but also the patterns and usage of applications to plan out which sites and application policies need to be developed.

Additionally, planning has to be done on how the SD-WAN edge device will interface to the existing infrastructure, if there will be hybrid WAN where some traffic will remain on the existing WAN infrastructure to plan the SD-WAN roll out.

They will also need real-time visibility into the new SD-WAN once it's running to verify that it's performing as expected and the performance data can be gotten from the SD-WAN system but may also require more granular visibility from a packet analytics to augment.

Over time, every NetOps team managing an SD-WAN will need holistic visibility of across the existing network infrastructure that feeds into the SD-WAN fabric to optimize, but also for troubleshooting where understanding the application path end-to-end in order to segment if the problem is in the SD-WAN or carrier or another portion of the network entirely. By monitoring the entire network through all three of these phase, IT can ensure a new SD-WAN project doesn't negatively affect business-critical applications.

As you can see, establishing end-to-end visibility and ensuring effective troubleshooting for complex, hybrid networks isn't easy. Nonetheless, it's essential for IT and NetOps to have these capabilities to support major network transformation projects. Using the techniques outlined above, IT and NetOps teams will be well-positioned to respond to application issues quickly and effectively, no matter what fabric of the network they originate from.

Related Links

Hot Topics

The Latest

Clearing the Path to AI: Why Vendor Consolidation Matters Now

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 ...

Cold Data, Hot Problem: Why AI Is Rewriting Enterprise Storage Strategy

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 ...

Observability at a Crossroads: AI, Economics, Complexity and the Enduring Power of Open Source

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 ...

Explainability Is the New Battleground in AI-Powered Observability

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 ...

Empowering the Human Side of the Autonomous IT Age

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 ...

4 Strategies for Ensuring Application Performance Across Hybrid Networks

Jay Botelho

As network transformation initiatives like SD-WAN, edge computing and public/private clouds are adopted at increasing rates, hybrid networks are quickly becoming the new normal for IT and NetOps professionals. According to a recent survey from Sirkin Research, 35% of enterprise networking and IT professionals struggle to access visibility into performance across all fabrics of the network. Without visibility into these hybrid network environments, NetOps are unable to troubleshoot the business-critical applications every organization relies on today.

Here are four ways IT and NetOps teams can gain better visibility into complex, hybrid networks:

Wireless Packet Capture

As personal devices and IoT becomes more common in the workplace, wireless issues are only going to increase. If you can't track performance across the entire end-to-end network, then you can't truly ensure end-user performance. Monitoring all wireless traffic isn't realistic for most organizations because it requires too many capture points spread throughout the wireless network to be viable.

A better approach is to supplement flow data and packet data from wired network segments with ad-hoc wireless packet capture for issues that can't be resolved based on the flow data alone. Sending a network engineer on-site to conduct a packet capture is one option, but it's extremely expensive. It's possible, with the right setup, to use a nearby AP as a sensor to sniff wireless traffic between a client and an access point for a short time. This isn't a common capability today, but I believe organizations need to start designing this into their networks to gain better visibility into the wireless network, and a better understanding of the hybrid network overall.

Packet Analysis for Complex Issues

There's a common adage in networking that says 80% of issues can be resolved using flow data and that the 20% that can't will require packet data. Why? Because many of these more complex problems have tons of potential causes.

For example, an end user complains that an application is running slowly. Maybe it's the network or possibly a QoS issue with the new SD-WAN that's been being rolled out. But the application could also be at fault; perhaps it wasn't perfectly designed and it's letting multiple users try to change an element of its database simultaneously, resulting in longer processing times. Without quick access to packet data, these difficult application issues can't be resolved successfully.

There are several free packet capture and analysis tools available, but larger organizations with complex networks will likely need to invest in a packet capture and analysis product that offers features like network mapping, customizable, pre-built reports, and visualizations to simplify this process and speed up troubleshooting.

Deep Packet Inspection as a Supplement to Flow Data

NetFlow and similar types of network telemetry data all have limitations. For example, when using NetFlow or IPFIX to troubleshoot VoIP calls, this data includes IP addresses, but not phone numbers. Customers calling to complain about VoIP will know their number, but probably not their IP address, so IT has no way to looking up the flows they need to hunt down the problem! With additional applications that offer deep packet inspection (DPI) it's possible to "add" new data points, such as the phone number of a VoIP call, to flow data. TCP retries is another useful data point that could be added. By adding selective data points to NetFlow, flow-based monitoring tools become much more useful for new situations that hybrid networks create.

Gather Data Necessary for SD-WAN Planning, Verification and Management

When transitioning to SD-WAN, enterprises need visibility into their existing network devices to determine the baseline of existing application performance, but also the patterns and usage of applications to plan out which sites and application policies need to be developed.

Additionally, planning has to be done on how the SD-WAN edge device will interface to the existing infrastructure, if there will be hybrid WAN where some traffic will remain on the existing WAN infrastructure to plan the SD-WAN roll out.

They will also need real-time visibility into the new SD-WAN once it's running to verify that it's performing as expected and the performance data can be gotten from the SD-WAN system but may also require more granular visibility from a packet analytics to augment.

Over time, every NetOps team managing an SD-WAN will need holistic visibility of across the existing network infrastructure that feeds into the SD-WAN fabric to optimize, but also for troubleshooting where understanding the application path end-to-end in order to segment if the problem is in the SD-WAN or carrier or another portion of the network entirely. By monitoring the entire network through all three of these phase, IT can ensure a new SD-WAN project doesn't negatively affect business-critical applications.

As you can see, establishing end-to-end visibility and ensuring effective troubleshooting for complex, hybrid networks isn't easy. Nonetheless, it's essential for IT and NetOps to have these capabilities to support major network transformation projects. Using the techniques outlined above, IT and NetOps teams will be well-positioned to respond to application issues quickly and effectively, no matter what fabric of the network they originate from.

Related Links

Hot Topics

The Latest

Clearing the Path to AI: Why Vendor Consolidation Matters Now

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 ...

Cold Data, Hot Problem: Why AI Is Rewriting Enterprise Storage Strategy

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 ...

Observability at a Crossroads: AI, Economics, Complexity and the Enduring Power of Open Source

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 ...

Explainability Is the New Battleground in AI-Powered Observability

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 ...

Empowering the Human Side of the Autonomous IT Age

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 ...