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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.

The Latest

Performance bottlenecks aren't uncommon when it comes to rolling out new technology, regardless of how capable or game-changing that technology might be. Every generation of new tech has encountered roadblocks that had to be overcome before it was truly able to shine. Virtualization forced organizations to rethink resource allocation, cloud transformation had us shift our focus toward scalability and elasticity, and microservices introduced entirely new challenges around observability and distributed systems. There's something different about AI, however ...

Consider a single order represented across order-management, execution, and settlement systems. Each database, message broker, and application may be online and processing its own records correctly. Yet the workflow has failed if related events arrive on different clocks, rely on inconsistent state, or cannot be reconciled before an operational decision must be made ...

AI now exists in almost every IT workflow. In a recent survey of more than 800 IT service professionals, all respondents indicated the use of AI in some form within their organization. But there's a growing paradox: if dashboards are clearing faster and alerts are resolved at unprecedented speed, why aren't IT service desks reporting lighter workloads? The research found that 71% of IT teams said their actual workload has remained flat or increased since adopting AI. This reality appears to contradict what we’ve been told about AI ...

Two years ago, almost every customer conversation about AI started with the same questions: Which model should we use? What can it do? Is it ready for the enterprise? Today, those discussions have moved on. CIOs are far more interested in how to govern AI, integrate it with existing systems, prepare their workforce and make it part of everyday operations. The challenge is no longer to prove that AI can deliver value. It's instead about how to embed AI into the business in a way that's secure, scalable and delivers measurable outcomes ...

Two things happened to production incidents between 2023 and now, and they did not happen at the same speed. The first is that a class of dependency that barely existed three years ago now accounts for one incident in ten. Incidents disclosed by AI model and AI application providers rose from 1.7% of all disclosed unplanned incidents in 2023 to 10.7% in 2026 year to date, roughly a sixfold rise; that counts only incidents at AI companies themselves, so the true share is higher. The second is that the time to close an incident has not come down ...

When an AI assistant gives an incomplete or incorrect answer, teams often blame the model. They adjust prompts, switch models, increase context windows or test a new retrieval strategy. However the model may not be a problem. In many enterprise AI workflows, the problem begins inside the document-ingestion pipeline ...

If you talk to any security or observability teams right now, they're all fighting the same fire: their tooling was built to ingest X, but their sources are pumping Y and soon to be doing Z. The knee-jerk reaction is always the same: we need more platform. However, this reaction is wrong. Let me explain why, because the solution to this problem is foundational, not financial. Instead of hurling yet more money at the problem, make sure you've done what's needed upstream ...

Rapid AI adoption and the unique ways AI workloads operate is redefining the scope and structure of what these teams must deliver. This shift is forcing organizations to rethink how they manage scale, automation, and control, according to The State of SRE and Platform Engineering 2026, a new report from Dynatrace ...

AI is usually talked about as a software tool, but it also depends heavily on the network behind it. Whether a company is using AI for chatbots, automation, monitoring, analytics, or employee support, all of that information has to move across the network in a reliable and secure way. That means AI is not just an application decision. It is also an infrastructure decision. Before organizations rush into AI, they should ask a simple question: Is our network ready to support it? ...

Enterprise AI often lacks governed access to where business processes actually execute. Without that access, AI agents may be able to reason, but they cannot operate reliably across enterprise workflows. For AI agents to effectively carry out workflows, they will require integration-layer context and controls. Organizations can implement these prerequisites by providing AI with managed access to the middleware layer ...

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

Performance bottlenecks aren't uncommon when it comes to rolling out new technology, regardless of how capable or game-changing that technology might be. Every generation of new tech has encountered roadblocks that had to be overcome before it was truly able to shine. Virtualization forced organizations to rethink resource allocation, cloud transformation had us shift our focus toward scalability and elasticity, and microservices introduced entirely new challenges around observability and distributed systems. There's something different about AI, however ...

Consider a single order represented across order-management, execution, and settlement systems. Each database, message broker, and application may be online and processing its own records correctly. Yet the workflow has failed if related events arrive on different clocks, rely on inconsistent state, or cannot be reconciled before an operational decision must be made ...

AI now exists in almost every IT workflow. In a recent survey of more than 800 IT service professionals, all respondents indicated the use of AI in some form within their organization. But there's a growing paradox: if dashboards are clearing faster and alerts are resolved at unprecedented speed, why aren't IT service desks reporting lighter workloads? The research found that 71% of IT teams said their actual workload has remained flat or increased since adopting AI. This reality appears to contradict what we’ve been told about AI ...

Two years ago, almost every customer conversation about AI started with the same questions: Which model should we use? What can it do? Is it ready for the enterprise? Today, those discussions have moved on. CIOs are far more interested in how to govern AI, integrate it with existing systems, prepare their workforce and make it part of everyday operations. The challenge is no longer to prove that AI can deliver value. It's instead about how to embed AI into the business in a way that's secure, scalable and delivers measurable outcomes ...

Two things happened to production incidents between 2023 and now, and they did not happen at the same speed. The first is that a class of dependency that barely existed three years ago now accounts for one incident in ten. Incidents disclosed by AI model and AI application providers rose from 1.7% of all disclosed unplanned incidents in 2023 to 10.7% in 2026 year to date, roughly a sixfold rise; that counts only incidents at AI companies themselves, so the true share is higher. The second is that the time to close an incident has not come down ...

When an AI assistant gives an incomplete or incorrect answer, teams often blame the model. They adjust prompts, switch models, increase context windows or test a new retrieval strategy. However the model may not be a problem. In many enterprise AI workflows, the problem begins inside the document-ingestion pipeline ...

If you talk to any security or observability teams right now, they're all fighting the same fire: their tooling was built to ingest X, but their sources are pumping Y and soon to be doing Z. The knee-jerk reaction is always the same: we need more platform. However, this reaction is wrong. Let me explain why, because the solution to this problem is foundational, not financial. Instead of hurling yet more money at the problem, make sure you've done what's needed upstream ...

Rapid AI adoption and the unique ways AI workloads operate is redefining the scope and structure of what these teams must deliver. This shift is forcing organizations to rethink how they manage scale, automation, and control, according to The State of SRE and Platform Engineering 2026, a new report from Dynatrace ...

AI is usually talked about as a software tool, but it also depends heavily on the network behind it. Whether a company is using AI for chatbots, automation, monitoring, analytics, or employee support, all of that information has to move across the network in a reliable and secure way. That means AI is not just an application decision. It is also an infrastructure decision. Before organizations rush into AI, they should ask a simple question: Is our network ready to support it? ...

Enterprise AI often lacks governed access to where business processes actually execute. Without that access, AI agents may be able to reason, but they cannot operate reliably across enterprise workflows. For AI agents to effectively carry out workflows, they will require integration-layer context and controls. Organizations can implement these prerequisites by providing AI with managed access to the middleware layer ...