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How Fluent Are You In Application-Aware Network Performance Management?

Nik Koutsoukos

Your enterprise network — and all the applications running on it — is the foundation for how every single employee gets his or her work done. E-mail, VoIP, CRM, ERP and every other custom or off-the-shelf application runs on your network. In order to provide these applications to end-users, more enterprises are adopting a hybrid enterprise model that incorporates a mix of on-premises and cloud-hosted apps, and of networks comprised of private, public Internet infrastructure.

This makes monitoring applications and network performance a lot more challenging, more time-consuming and therefore costlier for IT. Add in the need to maintain security and data integrity as user access and devices become increasingly diverse and staying on top of monitoring becomes extremely difficult. Achieving full end-to-end visibility requires implementing a holistic systems-based approach that provides all end-users at all locations with a reliable, secure and cost-efficient network and application experience. Moreover, determining whether or not you have this level of visibility requires you to first assess your "fluency" in application-aware network performance management.

Even as the sheer number of systems, devices, applications and endpoints IT must manage skyrockets, one thing remains unchanged: the best call or email from an end-user is the one that never comes. Users satisfied with performance and availability do not complain, but they won’t hesitate to do so as soon as something goes wrong. Simultaneously, they constantly increase the pressure on IT by demanding instant access and consistent application performance irrespective of their access device or their location. These complexities create serious risks to network uptime, information and data security, and regulatory compliance.

This leads us to a key question you must ask yourself when determining whether you have the necessary visibility into your network and all the applications running on it: "Do I know what I need to monitor?"

IDC finds that most organizations simply don’t know the types of applications, number of devices, or traffic sources on their enterprise networks (Source: IDC - Realizing Business Value and ROI with Application-Aware Network Performance Management July 2012. Overcoming that problem requires implementing a solution that provides multiple unified views of the network, application traffic, and actual end-user experience, and one that also conducts its own discovery, dependency mapping, and behavioral analysis. The goal is to be able to answer the following:

■ What’s on your network?

■ Who’s using it?

■ How are they using it?

■ Where are they accessing it?

■ When did this all take place?

Is "Performance" in Your Vocabulary?

If you are able to confidently answer all of the above questions then you are mostly there, but another critical factor to consider is whether you’re providing the levels of performance your end-users require.

The pervasive virtualization of data center resources combined with availability of APIs to control those resources, make a software-defined data center a possibility. This combination of virtualization and APIs allow for greater agility and improved efficiency, as data centers can now deliver the right resources at the right time.

Ensuring applications perform requires an understanding of three key requirements:

Visibility into layers of virtualization: Virtualization introduces layers of abstraction that can hide the details of what’s happening to an application. As physical systems get carved up into logical units, information about the physical system alone is insufficient. You need the ability to isolate performance issues within virtualized and physical environments.

Application performance infrastructure must also be virtualized: Pervasive virtualization is at the foundation of the software-defined data center. This improves utilization and reduces capital and operating costs. To maximize efficiency across your data center, you need virtual application delivery controllers, storage delivery controllers, WAN optimization controllers, and other application performance infrastructure.

API access to application performance infrastructure: In a software-defined data center, infrastructure is accessible and configurable through lines of code. That requires all components of your data center, including application performance infrastructure, to have APIs. APIs allow programmers to define what services are needed in their code, as well as integrate infrastructure with orchestration systems.

In summary, you must have the visibility to understand how specific users and events behave in order to ensure performance and quickly locate the root cause of any problem across the network. When an end user calls the help desk and reports that the network is slow, he or she won’t be able to help you identify which of any number of factors is hurting network performance. Network visibility and contextual tools usually reduce the number of calls and always reduce the amount of time to address the situation. Easy-to-use dashboards that clearly identify the source of the problem are a must-have in order to ensure your fluency in the complicated language of application-aware network performance management. Fortunately for enterprises today, a host of tools are now readily available, making it easier to become fluent in application-aware network performance management.

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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 ...

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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 ...

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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.

How Fluent Are You In Application-Aware Network Performance Management?

Nik Koutsoukos

Your enterprise network — and all the applications running on it — is the foundation for how every single employee gets his or her work done. E-mail, VoIP, CRM, ERP and every other custom or off-the-shelf application runs on your network. In order to provide these applications to end-users, more enterprises are adopting a hybrid enterprise model that incorporates a mix of on-premises and cloud-hosted apps, and of networks comprised of private, public Internet infrastructure.

This makes monitoring applications and network performance a lot more challenging, more time-consuming and therefore costlier for IT. Add in the need to maintain security and data integrity as user access and devices become increasingly diverse and staying on top of monitoring becomes extremely difficult. Achieving full end-to-end visibility requires implementing a holistic systems-based approach that provides all end-users at all locations with a reliable, secure and cost-efficient network and application experience. Moreover, determining whether or not you have this level of visibility requires you to first assess your "fluency" in application-aware network performance management.

Even as the sheer number of systems, devices, applications and endpoints IT must manage skyrockets, one thing remains unchanged: the best call or email from an end-user is the one that never comes. Users satisfied with performance and availability do not complain, but they won’t hesitate to do so as soon as something goes wrong. Simultaneously, they constantly increase the pressure on IT by demanding instant access and consistent application performance irrespective of their access device or their location. These complexities create serious risks to network uptime, information and data security, and regulatory compliance.

This leads us to a key question you must ask yourself when determining whether you have the necessary visibility into your network and all the applications running on it: "Do I know what I need to monitor?"

IDC finds that most organizations simply don’t know the types of applications, number of devices, or traffic sources on their enterprise networks (Source: IDC - Realizing Business Value and ROI with Application-Aware Network Performance Management July 2012. Overcoming that problem requires implementing a solution that provides multiple unified views of the network, application traffic, and actual end-user experience, and one that also conducts its own discovery, dependency mapping, and behavioral analysis. The goal is to be able to answer the following:

■ What’s on your network?

■ Who’s using it?

■ How are they using it?

■ Where are they accessing it?

■ When did this all take place?

Is "Performance" in Your Vocabulary?

If you are able to confidently answer all of the above questions then you are mostly there, but another critical factor to consider is whether you’re providing the levels of performance your end-users require.

The pervasive virtualization of data center resources combined with availability of APIs to control those resources, make a software-defined data center a possibility. This combination of virtualization and APIs allow for greater agility and improved efficiency, as data centers can now deliver the right resources at the right time.

Ensuring applications perform requires an understanding of three key requirements:

Visibility into layers of virtualization: Virtualization introduces layers of abstraction that can hide the details of what’s happening to an application. As physical systems get carved up into logical units, information about the physical system alone is insufficient. You need the ability to isolate performance issues within virtualized and physical environments.

Application performance infrastructure must also be virtualized: Pervasive virtualization is at the foundation of the software-defined data center. This improves utilization and reduces capital and operating costs. To maximize efficiency across your data center, you need virtual application delivery controllers, storage delivery controllers, WAN optimization controllers, and other application performance infrastructure.

API access to application performance infrastructure: In a software-defined data center, infrastructure is accessible and configurable through lines of code. That requires all components of your data center, including application performance infrastructure, to have APIs. APIs allow programmers to define what services are needed in their code, as well as integrate infrastructure with orchestration systems.

In summary, you must have the visibility to understand how specific users and events behave in order to ensure performance and quickly locate the root cause of any problem across the network. When an end user calls the help desk and reports that the network is slow, he or she won’t be able to help you identify which of any number of factors is hurting network performance. Network visibility and contextual tools usually reduce the number of calls and always reduce the amount of time to address the situation. Easy-to-use dashboards that clearly identify the source of the problem are a must-have in order to ensure your fluency in the complicated language of application-aware network performance management. Fortunately for enterprises today, a host of tools are now readily available, making it easier to become fluent in application-aware network performance management.

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.