Skip to main content

Reducing the Risks Associated with Deploying New Network-Centric Applications

Mike Heumann

It has been clear for quite some time that the network has become the lifeblood of nearly all enterprises. This is not just true for obvious network-centric enterprises such as retail sites or content distributors, but also for enterprises that utilize distributed applications such as SAP, Oracle, or any number of other network-centric applications.

Over the past few years, a number of new enterprise technologies have emerged that are critically reliant on network performance, but where this reliance is not necessarily obvious. These technologies include enterprise-class Voice over IP (VOIP) telephony solutions, Virtual Desktop Infrastructure (VDI) solutions, and enterprise collaboration tools.

While the vulnerability of "classical" distributed applications to network performance issues are well-understood, it is quite a different matter for a VDI session to momentarily "freeze", or for the CEO's VOIP call to get disrupted due to network issues. In short, these issues are far more visible than those associated with classical distributed applications, and as technologies such as software-defined networks (SDN) and hybrid private-public enterprise clouds become more prevalent, these issues are likely to become more rather than less pronounced.

So what do IT departments need to ensure that they can provide the levels of performance from these new technologies that users expect?

The most obvious answer is that they need to know what is really going on in their networks. While this sounds trite, it is far more difficult than one might expect. Causative factors such as microbursts, timeouts, and protocol errors can be difficult to detect with conventional application performance tools, and tying these causative events to the specific "new technology" outages can be even harder.

Given that many of these causative factors can be intermittent in nature certainly doesn't help. This is one of the primary reasons that many enterprises have introduced dedicated "network visibility fabrics" that provide instrumentation at key points in the network, exposing the full set of network packets and flow data that underlie these causative issues. While network visibility fabrics do not prevent these issues from occurring, they do speed the ability to resolve issues, which helps to avoid "outages" of network-centric technologies such as VDI, VoIP, network collaboration tools, and SDN frameworks.

As with most human endeavors, one of the best practices for making good decisions is to have the right data. Even the best decision-making processes can lead to wrong decisions by not having the right data. As networks (and the applications that depend on them) become more complex and carry more types of data, it becomes imperative to have the right data to avoid making guesses as to what is causing network issues. Look to see more enterprises implementing network visibility fabrics as dense 10Gb Ethernet networks become more prevalent, and more enterprises start to deploy these new technologies.

Mike Heumann is Sr. Director, Marketing (Endace) for Emulex.

Related Links:

www.emulex.com/

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 ...

Reducing the Risks Associated with Deploying New Network-Centric Applications

Mike Heumann

It has been clear for quite some time that the network has become the lifeblood of nearly all enterprises. This is not just true for obvious network-centric enterprises such as retail sites or content distributors, but also for enterprises that utilize distributed applications such as SAP, Oracle, or any number of other network-centric applications.

Over the past few years, a number of new enterprise technologies have emerged that are critically reliant on network performance, but where this reliance is not necessarily obvious. These technologies include enterprise-class Voice over IP (VOIP) telephony solutions, Virtual Desktop Infrastructure (VDI) solutions, and enterprise collaboration tools.

While the vulnerability of "classical" distributed applications to network performance issues are well-understood, it is quite a different matter for a VDI session to momentarily "freeze", or for the CEO's VOIP call to get disrupted due to network issues. In short, these issues are far more visible than those associated with classical distributed applications, and as technologies such as software-defined networks (SDN) and hybrid private-public enterprise clouds become more prevalent, these issues are likely to become more rather than less pronounced.

So what do IT departments need to ensure that they can provide the levels of performance from these new technologies that users expect?

The most obvious answer is that they need to know what is really going on in their networks. While this sounds trite, it is far more difficult than one might expect. Causative factors such as microbursts, timeouts, and protocol errors can be difficult to detect with conventional application performance tools, and tying these causative events to the specific "new technology" outages can be even harder.

Given that many of these causative factors can be intermittent in nature certainly doesn't help. This is one of the primary reasons that many enterprises have introduced dedicated "network visibility fabrics" that provide instrumentation at key points in the network, exposing the full set of network packets and flow data that underlie these causative issues. While network visibility fabrics do not prevent these issues from occurring, they do speed the ability to resolve issues, which helps to avoid "outages" of network-centric technologies such as VDI, VoIP, network collaboration tools, and SDN frameworks.

As with most human endeavors, one of the best practices for making good decisions is to have the right data. Even the best decision-making processes can lead to wrong decisions by not having the right data. As networks (and the applications that depend on them) become more complex and carry more types of data, it becomes imperative to have the right data to avoid making guesses as to what is causing network issues. Look to see more enterprises implementing network visibility fabrics as dense 10Gb Ethernet networks become more prevalent, and more enterprises start to deploy these new technologies.

Mike Heumann is Sr. Director, Marketing (Endace) for Emulex.

Related Links:

www.emulex.com/

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 ...