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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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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 ...

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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 ...

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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 ...

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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

Hot Topics

The Latest

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 ...