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AANPM: When the NMS is Not Enough

Application Aware Network Performance Management Offers A new approach to network monitoring
Bruce Kosbab

Organizations are becoming increasingly dependent on the performance of their critical business applications. These are continually developing to meet the changing needs of the business; new applications are created, new users and features added and new ways of accessing the applications introduced, such as BYOD.

However, no technology changes come without a price, and today’s complex applications put an increasing strain on the organization’s network and server infrastructure. Furthermore, user expectations of rapid response times mean that the network infrastructure is no longer just the "plumbing". It supports business-critical applications, provides the data on which decisions are made and facilitates communications with customers, partners, suppliers and co-workers, making it a strategic asset to the business. Any downtime or degradation in network or application performance will directly impact an organization’s bottom line.

Historically the network has been considered as a separate, well-defined entity, making it relatively straightforward to write tools to understand and analyze its performance. These fall into two categories: Network Management Systems (NMS) and packet capture and analysis tools.

Most NMS have been infrastructure focused, addressing device monitoring, capacity planning, configuration management, fault management, analysis of interface traffic etc. and ignoring the applications and data traversing the network. They do not perform analytics on application response time, TCP errors and other issues that impact applications.

Application Performance Management (APM) systems typically support auto-discovery of all the applications in the network, providing transaction analysis, application usage analysis, end-user experience analysis, user-defined transaction profiling and the basic functions to monitor the health and performance of all configured application infrastructure assets. However, if an application is running slowly they find it difficult to identify if the problem is application or network based.

Whereas separate systems were once sufficient to stay on top of problems, the increased interdependency of network and applications and cost of downtime means it is no longer enough to use a discrete tool and say "it’s not the network" or "my servers are fine". These tools are not designed to manage the interplay between network and applications environments, which needs to be understood and managed to optimize the user experience.

IT teams need to work together using correlated data to find the root cause and solve issues quickly before they impact the business.

Leveraging Application and Network Performance Methodologies

They require complete visibility of the network across all layers, from the data center to the branch office. The solution is AANPM: Application Aware Network Performance Management. AANPM is a method of monitoring, analyzing and troubleshooting both networks and applications. It takes an application-centric view of everything happening across the network, providing end-to-end visibility of the network and applications and their interdependencies, and enabling engineers to monitor and optimize the end user experience. It does not look at applications from a coding perspective, but in terms of how they are deployed and how they are performing.

By leveraging data points from both application and network performance methodologies, AANPM helps all branches of IT work together to ensure optimal performance of applications and network.

AANPM offers specific, tangible business benefits:

• End-to-end infrastructure visibility

• Faster problem-solving

• Improved user experience

• Enhanced productivity

• Cost savings

• Improved infrastructure optimization

• Better business understanding of IT

Bruce Kosbab is CTO of Fluke Networks.

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AANPM: When the NMS is Not Enough

Application Aware Network Performance Management Offers A new approach to network monitoring
Bruce Kosbab

Organizations are becoming increasingly dependent on the performance of their critical business applications. These are continually developing to meet the changing needs of the business; new applications are created, new users and features added and new ways of accessing the applications introduced, such as BYOD.

However, no technology changes come without a price, and today’s complex applications put an increasing strain on the organization’s network and server infrastructure. Furthermore, user expectations of rapid response times mean that the network infrastructure is no longer just the "plumbing". It supports business-critical applications, provides the data on which decisions are made and facilitates communications with customers, partners, suppliers and co-workers, making it a strategic asset to the business. Any downtime or degradation in network or application performance will directly impact an organization’s bottom line.

Historically the network has been considered as a separate, well-defined entity, making it relatively straightforward to write tools to understand and analyze its performance. These fall into two categories: Network Management Systems (NMS) and packet capture and analysis tools.

Most NMS have been infrastructure focused, addressing device monitoring, capacity planning, configuration management, fault management, analysis of interface traffic etc. and ignoring the applications and data traversing the network. They do not perform analytics on application response time, TCP errors and other issues that impact applications.

Application Performance Management (APM) systems typically support auto-discovery of all the applications in the network, providing transaction analysis, application usage analysis, end-user experience analysis, user-defined transaction profiling and the basic functions to monitor the health and performance of all configured application infrastructure assets. However, if an application is running slowly they find it difficult to identify if the problem is application or network based.

Whereas separate systems were once sufficient to stay on top of problems, the increased interdependency of network and applications and cost of downtime means it is no longer enough to use a discrete tool and say "it’s not the network" or "my servers are fine". These tools are not designed to manage the interplay between network and applications environments, which needs to be understood and managed to optimize the user experience.

IT teams need to work together using correlated data to find the root cause and solve issues quickly before they impact the business.

Leveraging Application and Network Performance Methodologies

They require complete visibility of the network across all layers, from the data center to the branch office. The solution is AANPM: Application Aware Network Performance Management. AANPM is a method of monitoring, analyzing and troubleshooting both networks and applications. It takes an application-centric view of everything happening across the network, providing end-to-end visibility of the network and applications and their interdependencies, and enabling engineers to monitor and optimize the end user experience. It does not look at applications from a coding perspective, but in terms of how they are deployed and how they are performing.

By leveraging data points from both application and network performance methodologies, AANPM helps all branches of IT work together to ensure optimal performance of applications and network.

AANPM offers specific, tangible business benefits:

• End-to-end infrastructure visibility

• Faster problem-solving

• Improved user experience

• Enhanced productivity

• Cost savings

• Improved infrastructure optimization

• Better business understanding of IT

Bruce Kosbab is CTO of Fluke Networks.

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 ...