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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

AI is becoming the operating system of the enterprise. It acts as an invisible coordination layer that understands intent, connects systems, and executes work across complex SaaS environments. Previously, employees had to click through multiple systems — CRM, ERP, support tools, collaboration platforms — to complete a single task. Now, instead of navigating each application manually, they can simply state what they need to accomplish ...

In 2026, the cost of downtime or an outage is no longer just a technical inconvenience; it's a $600 billion wake up call for global businesses. As our digital ecosystems become  more interconnected, each touchpoint introduces new risks and multiplies the consequences when things go wrong. And the data is clear: aggregate downtime costs  for Global 2,000 companies have surged 50% since 2024, reaching a staggering $600 billion ...

Deloitte found that 74% of enterprises expect to deploy agentic AI solutions in the next 24 months. However, the rush to deployment is outpacing foundational work, though. Only 21% of enterprises have fully formed agent governance models in place. The result? AI agents deployed without guidance or governance begin to function as fragmented islands of complexity ...

Cloud spending is no longer viewed as a passthrough IT expense, but as a strategic financial lever that directly impacts innovation capacity, profitability and enterprise resilience, according to the CFO Cloud Cost Optimization Report from Azul ...

As AI moves from generating responses to performing actions, the need for trust increases exponentially. And as organizations enlist AI agents for increasingly sophisticated business processes, trust is going to be the single most important theme for spurring adoption. What can organizations do to build trustworthy AI agents? ...

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 ...