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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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In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

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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 ...

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 ...

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 ...

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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 ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

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

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

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 ...

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 ...

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 ...

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 ...

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 ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.