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Solving Application Performance Issues with Multi-Segment Analysis

Chris Bloom

Enterprises are increasingly relying on a variety of locally hosted, web- or cloud-based applications for business-critical tasks, making uninterrupted application performance a must-have for business continuity. For that reason, unplanned network disruptions mean business disruptions, and the severe cases can often lead to financial losses and even legal consequences. Burdened with the task of keeping all of an enterprise's network and its applications, clients and servers up and running at peak performance, network engineers require tools and processes that make this task possible.

With today's distributed application architectures becoming more common, a technique called multi-segment analysis, can greatly help IT professionals pinpoint the location and cause of latency or other application performance issues.

What is Multi-Segment Analysis (MSA)?

In the past, all of the data needed to conduct an analysis of centrally-located applications could be gathered in real time from that single location. With distributed application architectures, the same data is required. But multiple network links, or hops, must be analyzed to get the full picture. Once the issue is isolated, you still needed to determine whether it's the application or the network. If it's the network, what network link is it occurring on? When troubleshooting application performance problems for users at a remote site, the IT team would ideally have access to data collected at the remote office internet connection and at the data center, to give a holistic view of the issue.

By helping IT professionals gather the necessary data from multiple network links, multi-segment analysis provides the solution to troubleshooting application issues.

How Does MSA Work?

Multi-segment analysis is a post-capture method that automates and simplifies the process of gathering and visualizing network data from multiple network segments and/or multi-tiered applications. This technique correlates the data across various network segments, finding common elements so that individual application transactions can be reassembled from a network perspective, then visualized and analyzed to indicate potential problem areas.

MSA provides a clear view of the application flow, including network and transaction latency, application, turn times, packet retransmissions, and dropped packets. Armed with this depth of information, network engineers can easily pinpoint any application anomalies at the client, server, or on the network.

Deploying MSA-Capable Devices at Multiple Points is Key

Multi-segment analysis requires at least two capture points to work. In fact, the accuracy of MSA improves significantly when additional measurement points are placed at strategic points along the network.

Most enterprises already have highly capable network monitoring appliances deployed at their data centers or corporate offices, so remote or branch offices with limited network bandwidth only require a small network monitoring appliance as an economical way to collect network data. With an appliance at each remote office, these supplementary measurement points can be used to measure network latency between any point, such as a remote office, and the data center.

One additional consideration is whether to adopt a passive or an active solution. If the solution being deployed is "active," it may generate a lot of test traffic on the network that can exacerbate existing latency problems if not managed properly. A passive system, on the other hand, does not generate additional network traffic; it monitors and measures real traffic to identify and flag problems only when they occur.

Conclusion

Multi-segment analysis is a valuable tool in any IT professional's arsenal, accelerating the MTTR of application-level issues. Through experience it is possible to automate the process of gathering network data from multiple, strategically located network segments, and/or multi-tiered applications. In short, MSA makes the troubleshooting process much simpler and helps network engineers achieve an uninterrupted and granular view of the network.

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Solving Application Performance Issues with Multi-Segment Analysis

Chris Bloom

Enterprises are increasingly relying on a variety of locally hosted, web- or cloud-based applications for business-critical tasks, making uninterrupted application performance a must-have for business continuity. For that reason, unplanned network disruptions mean business disruptions, and the severe cases can often lead to financial losses and even legal consequences. Burdened with the task of keeping all of an enterprise's network and its applications, clients and servers up and running at peak performance, network engineers require tools and processes that make this task possible.

With today's distributed application architectures becoming more common, a technique called multi-segment analysis, can greatly help IT professionals pinpoint the location and cause of latency or other application performance issues.

What is Multi-Segment Analysis (MSA)?

In the past, all of the data needed to conduct an analysis of centrally-located applications could be gathered in real time from that single location. With distributed application architectures, the same data is required. But multiple network links, or hops, must be analyzed to get the full picture. Once the issue is isolated, you still needed to determine whether it's the application or the network. If it's the network, what network link is it occurring on? When troubleshooting application performance problems for users at a remote site, the IT team would ideally have access to data collected at the remote office internet connection and at the data center, to give a holistic view of the issue.

By helping IT professionals gather the necessary data from multiple network links, multi-segment analysis provides the solution to troubleshooting application issues.

How Does MSA Work?

Multi-segment analysis is a post-capture method that automates and simplifies the process of gathering and visualizing network data from multiple network segments and/or multi-tiered applications. This technique correlates the data across various network segments, finding common elements so that individual application transactions can be reassembled from a network perspective, then visualized and analyzed to indicate potential problem areas.

MSA provides a clear view of the application flow, including network and transaction latency, application, turn times, packet retransmissions, and dropped packets. Armed with this depth of information, network engineers can easily pinpoint any application anomalies at the client, server, or on the network.

Deploying MSA-Capable Devices at Multiple Points is Key

Multi-segment analysis requires at least two capture points to work. In fact, the accuracy of MSA improves significantly when additional measurement points are placed at strategic points along the network.

Most enterprises already have highly capable network monitoring appliances deployed at their data centers or corporate offices, so remote or branch offices with limited network bandwidth only require a small network monitoring appliance as an economical way to collect network data. With an appliance at each remote office, these supplementary measurement points can be used to measure network latency between any point, such as a remote office, and the data center.

One additional consideration is whether to adopt a passive or an active solution. If the solution being deployed is "active," it may generate a lot of test traffic on the network that can exacerbate existing latency problems if not managed properly. A passive system, on the other hand, does not generate additional network traffic; it monitors and measures real traffic to identify and flag problems only when they occur.

Conclusion

Multi-segment analysis is a valuable tool in any IT professional's arsenal, accelerating the MTTR of application-level issues. Through experience it is possible to automate the process of gathering network data from multiple, strategically located network segments, and/or multi-tiered applications. In short, MSA makes the troubleshooting process much simpler and helps network engineers achieve an uninterrupted and granular view of the network.

Hot Topics

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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 ...

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.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...

The gap is widening between what teams spend on observability tools and the value they receive amid surging data volumes and budget pressures, according to The Breaking Point for Observability Leaders, a report from Imply ...

Seamless shopping is a basic demand of today's boundaryless consumer — one with little patience for friction, limited tolerance for disconnected experiences and minimal hesitation in switching brands. Customers expect intuitive, highly personalized experiences and the ability to move effortlessly across physical and digital channels within the same journey. Failure to deliver can cost dearly ...

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