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Fault Domain Isolation Key to Avoiding Network Blame Game - Part 1

Jeff Brown

The team-of-experts approach to incident response was effective when network problems were less complex and everyone was part of the same organization. However, in recent years the process required for Root Cause Analysis (RCA) of network events and business application performance issues has become more difficult, obscured by infrastructural cloudiness and stakeholders residing in disparate departments, companies and geographies. 
 
For many organizations, the task of quickly identifying root cause has become paramount to meeting Service Level Agreements (SLAs) and preventing customer churn. Yet, according to the Emulex Visibility Study, 79 percent of organizations have had events attributed to the wrong IT group, adding confusion and delays to the resolution of these issues.
 
This two-part series will explain a more fact-based, packet-analysis driven approach to Fault Domain Isolation (FDI), which is helping organizations troubleshoot and resolve network and application performance incidents.

Outsourcing Takes Over

It was hard enough getting visibility into what was actually happening when the entire infrastructure was owned and controlled by a single organization. With the rapid expansion of outsourcing, there are a growing number of blind spots developing throughout end-to-end business applications. When an entire technology tier is outsourced, what you have is a massive blind spot keeping you from performing root cause analysis within that technology domain. To accommodate outsourced technology, organizations must clearly define the purpose and requirements of the Fault Domain Isolation stage of the incident response workflow compared to the Root Cause Analysis stage.

Understanding FDI

The motivation behind FDI is easy to understand because anyone who’s gone to the doctor has seen it in action. An “incident investigation” in healthcare typically starts with a process that is essentially FDI. A general practitioner performs an initial assessment, orders diagnostic tests, and evaluates the results. The patient is sent to a specialist for additional diagnosis and treatment only if there is sufficient evidence to justify it. Facts, not guesswork, drive the diagnostic process.

Organizations that deploy FDI seek to minimize the number and type of technology experts involved in each incident, which is why FDI should precede RCA. The goal is to identify exactly one suspect technology tier before starting the deep dive search for root cause.

Why isolate by technology? Because that is how departments (and outsourcing) are typically organized, and how you quickly reduce the number of people involved. By implicating just one fault domain, you eliminate entire departments and external organizations from being tied up in the investigation; just as you wouldn’t pull in a neurosurgeon to examine a broken toe!

A key goal of FDI is to stop the “passing the buck” phenomenon in its tracks. For FDI to be effective it must provide irrefutable evidence that root cause lies in the “suspect” sub-system or technology tier, and just as importantly, that the same evidence confirms root cause is highly unlikely to lie anywhere else. This is especially important when the fault domain lies in an outsourced technology.

When handing the problem over to the responsible team or service provider, effective FDI also provides technology-specific, actionable data. It supplies the context, symptoms, and information needed for the technology team to immediately begin their deep dive search for root cause within the system for which they are responsible.

Exactly One Set of Facts

In order to be efficient and effective, FDI requires its analysis to be based on the actual packet data exchanged between the technology tiers. Packets don’t lie, nor do they obscure the critical details in averages or statistics. And having the underlying packets as evidence ensures the FDI process assigns irrefutable responsibility to the faulty technology tier.

Primary FDI – the act of assigning the incident to a specific technology team or outsourced service provider – is exceedingly cost effective to implement because its goal is relatively modest: to allocate incidents among a handful of departments or teams, plus any outsourced services. In practice, it involves relatively few technology tiers, a manageable number of tap points in the network, and a few network recorders monitoring between each technology tier.

Read Part 2 of this Blog, which identifies some of the hang ups of adopting FDI, as well as best practices.

Jeff Brown is Global Director of Training, NVP at Emulex.

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Fault Domain Isolation Key to Avoiding Network Blame Game - Part 1

Jeff Brown

The team-of-experts approach to incident response was effective when network problems were less complex and everyone was part of the same organization. However, in recent years the process required for Root Cause Analysis (RCA) of network events and business application performance issues has become more difficult, obscured by infrastructural cloudiness and stakeholders residing in disparate departments, companies and geographies. 
 
For many organizations, the task of quickly identifying root cause has become paramount to meeting Service Level Agreements (SLAs) and preventing customer churn. Yet, according to the Emulex Visibility Study, 79 percent of organizations have had events attributed to the wrong IT group, adding confusion and delays to the resolution of these issues.
 
This two-part series will explain a more fact-based, packet-analysis driven approach to Fault Domain Isolation (FDI), which is helping organizations troubleshoot and resolve network and application performance incidents.

Outsourcing Takes Over

It was hard enough getting visibility into what was actually happening when the entire infrastructure was owned and controlled by a single organization. With the rapid expansion of outsourcing, there are a growing number of blind spots developing throughout end-to-end business applications. When an entire technology tier is outsourced, what you have is a massive blind spot keeping you from performing root cause analysis within that technology domain. To accommodate outsourced technology, organizations must clearly define the purpose and requirements of the Fault Domain Isolation stage of the incident response workflow compared to the Root Cause Analysis stage.

Understanding FDI

The motivation behind FDI is easy to understand because anyone who’s gone to the doctor has seen it in action. An “incident investigation” in healthcare typically starts with a process that is essentially FDI. A general practitioner performs an initial assessment, orders diagnostic tests, and evaluates the results. The patient is sent to a specialist for additional diagnosis and treatment only if there is sufficient evidence to justify it. Facts, not guesswork, drive the diagnostic process.

Organizations that deploy FDI seek to minimize the number and type of technology experts involved in each incident, which is why FDI should precede RCA. The goal is to identify exactly one suspect technology tier before starting the deep dive search for root cause.

Why isolate by technology? Because that is how departments (and outsourcing) are typically organized, and how you quickly reduce the number of people involved. By implicating just one fault domain, you eliminate entire departments and external organizations from being tied up in the investigation; just as you wouldn’t pull in a neurosurgeon to examine a broken toe!

A key goal of FDI is to stop the “passing the buck” phenomenon in its tracks. For FDI to be effective it must provide irrefutable evidence that root cause lies in the “suspect” sub-system or technology tier, and just as importantly, that the same evidence confirms root cause is highly unlikely to lie anywhere else. This is especially important when the fault domain lies in an outsourced technology.

When handing the problem over to the responsible team or service provider, effective FDI also provides technology-specific, actionable data. It supplies the context, symptoms, and information needed for the technology team to immediately begin their deep dive search for root cause within the system for which they are responsible.

Exactly One Set of Facts

In order to be efficient and effective, FDI requires its analysis to be based on the actual packet data exchanged between the technology tiers. Packets don’t lie, nor do they obscure the critical details in averages or statistics. And having the underlying packets as evidence ensures the FDI process assigns irrefutable responsibility to the faulty technology tier.

Primary FDI – the act of assigning the incident to a specific technology team or outsourced service provider – is exceedingly cost effective to implement because its goal is relatively modest: to allocate incidents among a handful of departments or teams, plus any outsourced services. In practice, it involves relatively few technology tiers, a manageable number of tap points in the network, and a few network recorders monitoring between each technology tier.

Read Part 2 of this Blog, which identifies some of the hang ups of adopting FDI, as well as best practices.

Jeff Brown is Global Director of Training, NVP at Emulex.

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.