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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/

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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

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 ...