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Fault Domain Isolation Key to Avoiding Network Blame Game - Part 2

Jeff Brown

Start with Part 1 of this Blog

What’s the Hold Up?

It always reduces costs and decreases time-to-resolution when root cause analysis is being done in earnest, with confidence (and perhaps a bit of guilt) that the problem simply cannot lay elsewhere. RCA works best when the people working on the problem have the expertise to properly evaluate the cause and resolve the problem.

In Part 1 of this Blog, I explained how a packet-driven FDI process is an effective way to accelerate incident investigations and reduce the number of people involved. Further, to achieve its primary goal of getting only the right people involved in the incident investigation, we know it doesn’t take a lot of taps and equipment to isolate the major technology tiers. So why do team-of-expert meetings still persist in so many major incident investigations?

The problem might be that some simply do not believe that complex incidents can be fully resolved with just a few taps and some network recorders. And you know what, they’re right! But that isn’t the goal of the FDI stage of the incident investigation process. The goal is fault isolation, and that can be done simply and reliably. All you need is the underlying packets and a process to analyze them.

Divide and Conquer

The primary or first-layer FDI process isolates the incident to a single technology tier as defined by the organization’s internal structure and outsourcing arrangement.

Primary FDI is best achieved by:

1. Using network recording tools to monitor and store the network traffic occurring between technology tiers

2. Applying application transaction analysis to perform fault isolation.

Packet storage (rather than just averages or summaries) is key to enabling the back-in-time analysis upon which efficient FDI depends.

As you’ve probably guessed, FDI is a divide and conquer process that can be deployed in layers. FDI can also be used within each tier to further isolate the problem until highly efficient RCA can be done. This can be called intra-tier FDI, or perhaps secondary FDI.

Not surprisingly, network incident investigations are particularly amenable to a secondary FDI workflow, and once again, this is best achieved by monitoring and storing the actual packet flows between key network components for efficient back-in-time analysis.

It is valid to ask where the network tap points and network recording tools should be deployed when intra-network FDI is the goal. The main difference between primary FDI and intra-network FDI is that the location of the observation points is less an organizational issue, and more about physical location, technology, staff expertise, and of course, level of outsourcing and external suppliers. But the FDI process is similar: use packet-based analysis to provide irrefutable evidence as to which technology or service provider is at fault, and which are not.

Always-On or Always-Available?

You do not want to wait for a major incident to occur before you start deploying the tap points and monitoring tools needed for performing FDI -- that would defeat its purpose. So it seems pretty clear that the tap points and network recording tools needed for primary or first-level FDI should be deployed and running all the time. Those are your always-on appliances.

But what about secondary or intra-technology FDI? What about remote sites, regional data centers, and non-critical applications? You can’t tap everywhere, nor can you store everything.

Fortunately many network recording tools have been built to satisfy the needs of the always-on recording required between primary technology tiers, and the “always-available” recording connected via Network Packet Brokers to a plethora of secondary tap points. Always-available appliances do not necessarily give you long-term back-in-time visibility, but they can be quickly configured to begin monitoring where needed, on demand, tuned to the specific visibility needs of the incident investigation underway.

How Simple Is It?

So, is FDI truly as simple as we’ve described? Well, yes and no. Obviously there are plenty of unusual, complex, and just plain bizarre problems that can appear in a system as complex and dynamic as a modern organization’s networked business application infrastructure. And these types of problems will always require deep investigation, and the skills and knowledge of specialists and experts to resolve. But that doesn’t render FDI irrelevant or ineffective for these complex issues. Indeed it makes the need for a rigorous, repeatable, data-driven FDI process all the more important. Put another way, for complex problems why wouldn’t you use a proven divide and conquer approach like FDI?

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

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Fault Domain Isolation Key to Avoiding Network Blame Game - Part 2

Jeff Brown

Start with Part 1 of this Blog

What’s the Hold Up?

It always reduces costs and decreases time-to-resolution when root cause analysis is being done in earnest, with confidence (and perhaps a bit of guilt) that the problem simply cannot lay elsewhere. RCA works best when the people working on the problem have the expertise to properly evaluate the cause and resolve the problem.

In Part 1 of this Blog, I explained how a packet-driven FDI process is an effective way to accelerate incident investigations and reduce the number of people involved. Further, to achieve its primary goal of getting only the right people involved in the incident investigation, we know it doesn’t take a lot of taps and equipment to isolate the major technology tiers. So why do team-of-expert meetings still persist in so many major incident investigations?

The problem might be that some simply do not believe that complex incidents can be fully resolved with just a few taps and some network recorders. And you know what, they’re right! But that isn’t the goal of the FDI stage of the incident investigation process. The goal is fault isolation, and that can be done simply and reliably. All you need is the underlying packets and a process to analyze them.

Divide and Conquer

The primary or first-layer FDI process isolates the incident to a single technology tier as defined by the organization’s internal structure and outsourcing arrangement.

Primary FDI is best achieved by:

1. Using network recording tools to monitor and store the network traffic occurring between technology tiers

2. Applying application transaction analysis to perform fault isolation.

Packet storage (rather than just averages or summaries) is key to enabling the back-in-time analysis upon which efficient FDI depends.

As you’ve probably guessed, FDI is a divide and conquer process that can be deployed in layers. FDI can also be used within each tier to further isolate the problem until highly efficient RCA can be done. This can be called intra-tier FDI, or perhaps secondary FDI.

Not surprisingly, network incident investigations are particularly amenable to a secondary FDI workflow, and once again, this is best achieved by monitoring and storing the actual packet flows between key network components for efficient back-in-time analysis.

It is valid to ask where the network tap points and network recording tools should be deployed when intra-network FDI is the goal. The main difference between primary FDI and intra-network FDI is that the location of the observation points is less an organizational issue, and more about physical location, technology, staff expertise, and of course, level of outsourcing and external suppliers. But the FDI process is similar: use packet-based analysis to provide irrefutable evidence as to which technology or service provider is at fault, and which are not.

Always-On or Always-Available?

You do not want to wait for a major incident to occur before you start deploying the tap points and monitoring tools needed for performing FDI -- that would defeat its purpose. So it seems pretty clear that the tap points and network recording tools needed for primary or first-level FDI should be deployed and running all the time. Those are your always-on appliances.

But what about secondary or intra-technology FDI? What about remote sites, regional data centers, and non-critical applications? You can’t tap everywhere, nor can you store everything.

Fortunately many network recording tools have been built to satisfy the needs of the always-on recording required between primary technology tiers, and the “always-available” recording connected via Network Packet Brokers to a plethora of secondary tap points. Always-available appliances do not necessarily give you long-term back-in-time visibility, but they can be quickly configured to begin monitoring where needed, on demand, tuned to the specific visibility needs of the incident investigation underway.

How Simple Is It?

So, is FDI truly as simple as we’ve described? Well, yes and no. Obviously there are plenty of unusual, complex, and just plain bizarre problems that can appear in a system as complex and dynamic as a modern organization’s networked business application infrastructure. And these types of problems will always require deep investigation, and the skills and knowledge of specialists and experts to resolve. But that doesn’t render FDI irrelevant or ineffective for these complex issues. Indeed it makes the need for a rigorous, repeatable, data-driven FDI process all the more important. Put another way, for complex problems why wouldn’t you use a proven divide and conquer approach like FDI?

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

Hot Topics

The Latest

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 ...

If your best engineers spend their days sorting tickets and resetting access, you are wasting talent. New global data shows that employees in the IT sector rank among the least motivated across industries. They're under a lot of pressure from many angles. Pressure to upskill and uncertainty around what agentic AI means for job security is creating anxiety. Meanwhile, these roles often function like an on-call job and require many repetitive tasks ...