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Seven Tips for Optimizing Network Performance - Part 1

Jay Botelho

The network has grown increasingly complex within an incredibly short amount of time — and it's only getting more complicated with each passing day. In fact, according to Enterprise Strategy Group, 66% of organizations view their IT environments as more or significantly more complex than they were two years ago. This has put increasing pressure on networking teams to have increased visibility across new network landscapes and to solve problems quickly. But sorting through the mountain of alerts, trouble tickets and traffic to isolate whether a problem is the network or an application can be a daunting task.

Despite careful planning and monitoring, users still experience stuttering video calls, slow downloads, and dropped calls — all symptoms of common network problems. That's why proactive monitoring and optimization of the network is critical to keeping business operations running optimally. To help, let's look at some network performance management tips that can keep your team ahead of the curve.

1. Continuously Monitor Network Performance

With infrastructure now pushing into the cloud, new technologies like SD-WAN and SASE being a reality, having real-time insight into how traffic is moving across the extended network (including with remote workers) is basic table stakes. This rapid rise of new technologies has left some network performance monitoring solutions in the dust, and as discussed above, there's no management without monitoring. These legacy solutions have a clear focus (and strength) in data center monitoring, but fall short in areas like SD-WAN and oftentimes have nothing significant to offer regarding cloud monitoring.

Plan for upgrading these monitoring systems, including vendor migration if necessary, as part of your infrastructure upgrade, and find a single solution that can monitor your entire infrastructure. Too often the monitoring system update is trumped by the infrastructure upgrade, resulting in blind spots and reducing the effectiveness of determining the success of the infrastructure upgrade, not to mention the ability to troubleshoot issues with the new infrastructure.

2. Compare Network Performance

How can you tell if your infrastructure updates have improved your network performance if you don't have good data on the performance of your current infrastructure? The ability to compare baseline performance before and after a network change is the way network engineers measure success. The data that drives these baseline comparisons comes from network monitoring solutions.

Having a monitoring solution that best meets your needs in place before making network changes, especially major infrastructure changes, will set you up for success.

3. Determine When the Network is at Fault

When problems do occur, quick remediation is expected. There are often debates over whether it's the network or the application team's responsibility. Flow-based network monitoring data can provide some insight into the network vs. application question, but supplementing that with network packet data, and having it all available in a single solution, is the best way to isolate the problem.

Once you've isolated a network flow in question, packet data almost always provides clear evidence of whether the network or the application is at fault. Packet data provide a packet-by-packet view of the conversation — you can see every request, response, acknowledgement, etc. By reviewing the packets in the conversation, you can easily see what the network response times are, and the application response times.

If you see quick network acknowledgements, and then see long delays in getting any packets with data, it's a clear indication of an application problem and not a network problem. And packets provide the bonus of having detailed information in the payloads. Assuming the traffic is unencrypted, or can be unencrypted, packet payloads provide clues as to what is happening in the application, usually in the form of error messages embedded in the packet payloads.

Go to Seven Tips for Optimizing Network Performance - Part 2, with more tips for optimizing network performance

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AI is usually talked about as a software tool, but it also depends heavily on the network behind it. Whether a company is using AI for chatbots, automation, monitoring, analytics, or employee support, all of that information has to move across the network in a reliable and secure way. That means AI is not just an application decision. It is also an infrastructure decision. Before organizations rush into AI, they should ask a simple question: Is our network ready to support it? ...

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Enterprise networks rarely behave the same way for very long. A routing adjustment in one region may unexpectedly alter application performance in another. A cloud migration may introduce hidden dependencies that go unnoticed until an outage occurs. All the while, the network is managed by several different teams, each of whom use different tool sets — and as a result, have different views of the network ... There’s usually an engineer who remembers why traffic fails over a certain way between sites, or which transparent firewall was added where. The problem is that human memory cannot scale alongside enterprise-scale networks ...

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This year, many of the cloud infrastructure contracts signed in the early days of the AI boom will come up for renewal. As the year goes on, I anticipate we'll see a significant amount of cloud vendor swapouts and multi-cloud adoption, and the reason isn't just GPU depreciation. It's because they're tired of their current cloud providers ...

There's a moment the many observability teams have experienced days into bringing a new service into production: you realize that the vendor's claims of "intelligent" behavior included a large serving of hype. Their dashboards look nice until they don't, the failure modes are a black box, and no one on the team can confidently explain why the system did what it did at 2 am. Agentic AI is about to force every Ops team to relive that moment at web-scale until they start treating these systems as the dependencies they actually are ...

Seven Tips for Optimizing Network Performance - Part 1

Jay Botelho

The network has grown increasingly complex within an incredibly short amount of time — and it's only getting more complicated with each passing day. In fact, according to Enterprise Strategy Group, 66% of organizations view their IT environments as more or significantly more complex than they were two years ago. This has put increasing pressure on networking teams to have increased visibility across new network landscapes and to solve problems quickly. But sorting through the mountain of alerts, trouble tickets and traffic to isolate whether a problem is the network or an application can be a daunting task.

Despite careful planning and monitoring, users still experience stuttering video calls, slow downloads, and dropped calls — all symptoms of common network problems. That's why proactive monitoring and optimization of the network is critical to keeping business operations running optimally. To help, let's look at some network performance management tips that can keep your team ahead of the curve.

1. Continuously Monitor Network Performance

With infrastructure now pushing into the cloud, new technologies like SD-WAN and SASE being a reality, having real-time insight into how traffic is moving across the extended network (including with remote workers) is basic table stakes. This rapid rise of new technologies has left some network performance monitoring solutions in the dust, and as discussed above, there's no management without monitoring. These legacy solutions have a clear focus (and strength) in data center monitoring, but fall short in areas like SD-WAN and oftentimes have nothing significant to offer regarding cloud monitoring.

Plan for upgrading these monitoring systems, including vendor migration if necessary, as part of your infrastructure upgrade, and find a single solution that can monitor your entire infrastructure. Too often the monitoring system update is trumped by the infrastructure upgrade, resulting in blind spots and reducing the effectiveness of determining the success of the infrastructure upgrade, not to mention the ability to troubleshoot issues with the new infrastructure.

2. Compare Network Performance

How can you tell if your infrastructure updates have improved your network performance if you don't have good data on the performance of your current infrastructure? The ability to compare baseline performance before and after a network change is the way network engineers measure success. The data that drives these baseline comparisons comes from network monitoring solutions.

Having a monitoring solution that best meets your needs in place before making network changes, especially major infrastructure changes, will set you up for success.

3. Determine When the Network is at Fault

When problems do occur, quick remediation is expected. There are often debates over whether it's the network or the application team's responsibility. Flow-based network monitoring data can provide some insight into the network vs. application question, but supplementing that with network packet data, and having it all available in a single solution, is the best way to isolate the problem.

Once you've isolated a network flow in question, packet data almost always provides clear evidence of whether the network or the application is at fault. Packet data provide a packet-by-packet view of the conversation — you can see every request, response, acknowledgement, etc. By reviewing the packets in the conversation, you can easily see what the network response times are, and the application response times.

If you see quick network acknowledgements, and then see long delays in getting any packets with data, it's a clear indication of an application problem and not a network problem. And packets provide the bonus of having detailed information in the payloads. Assuming the traffic is unencrypted, or can be unencrypted, packet payloads provide clues as to what is happening in the application, usually in the form of error messages embedded in the packet payloads.

Go to Seven Tips for Optimizing Network Performance - Part 2, with more tips for optimizing network performance

Hot Topics

The Latest

Rapid AI adoption and the unique ways AI workloads operate is redefining the scope and structure of what these teams must deliver. This shift is forcing organizations to rethink how they manage scale, automation, and control, according to The State of SRE and Platform Engineering 2026, a new report from Dynatrace ...

AI is usually talked about as a software tool, but it also depends heavily on the network behind it. Whether a company is using AI for chatbots, automation, monitoring, analytics, or employee support, all of that information has to move across the network in a reliable and secure way. That means AI is not just an application decision. It is also an infrastructure decision. Before organizations rush into AI, they should ask a simple question: Is our network ready to support it? ...

Enterprise AI often lacks governed access to where business processes actually execute. Without that access, AI agents may be able to reason, but they cannot operate reliably across enterprise workflows. For AI agents to effectively carry out workflows, they will require integration-layer context and controls. Organizations can implement these prerequisites by providing AI with managed access to the middleware layer ...

Enterprise networks rarely behave the same way for very long. A routing adjustment in one region may unexpectedly alter application performance in another. A cloud migration may introduce hidden dependencies that go unnoticed until an outage occurs. All the while, the network is managed by several different teams, each of whom use different tool sets — and as a result, have different views of the network ... There’s usually an engineer who remembers why traffic fails over a certain way between sites, or which transparent firewall was added where. The problem is that human memory cannot scale alongside enterprise-scale networks ...

Ask an infrastructure team how confident they are in their ability to govern AI, and most will tell you they've got it handled. A recent survey of 406 IT decision-makers and platform engineering leaders found 86% expressing exactly that confidence. Ask the same group whether they have a formal written AI governance policy, and the number drops to 30%, according to Spacelift's Infrastructure Automation Report ...

In MEAN TIME TO INSIGHT Episode 27, Shamus McGillicuddy, EMA VP of Research, Network Infrastructure and Operations, and Parker Hathcock, EMA Research Director covering IT Service/Operations (ServiceOps), discuss observability unification in modern IT operations ... 

Virtual Private Networks became a cornerstone of enterprise security at a time when corporate infrastructure looked very different from today ... For years, this model worked well. But the architecture behind VPNs assumed a centralized corporate environment—one where the network itself was the hub of activity. In a cloud — first world, that assumption no longer holds ...

Website outages get resolved just as fast in August as they do in November. I went looking for the opposite: the summer slowdown everyone assumes is there once the people who fix things are away. It isn't in the data we collected, covering 1.8 million confirmed outages across tens of thousands of websites ...

This year, many of the cloud infrastructure contracts signed in the early days of the AI boom will come up for renewal. As the year goes on, I anticipate we'll see a significant amount of cloud vendor swapouts and multi-cloud adoption, and the reason isn't just GPU depreciation. It's because they're tired of their current cloud providers ...

There's a moment the many observability teams have experienced days into bringing a new service into production: you realize that the vendor's claims of "intelligent" behavior included a large serving of hype. Their dashboards look nice until they don't, the failure modes are a black box, and no one on the team can confidently explain why the system did what it did at 2 am. Agentic AI is about to force every Ops team to relive that moment at web-scale until they start treating these systems as the dependencies they actually are ...