Improve Your Performance with Application Intelligence

Keith Bromley

If you are like most IT professionals, which I am sure you are, you are dealing with a lot issues.

Typical issues include:

■ Constantly changing security threats to your network

■ An internal and external emphasis on your customer quality of experience

■ A greater need to troubleshoot problems faster

These three requirements are forcing IT to acquire an even better insight and understanding of their network to maximize its performance. One thing you can do to accomplish these goals is to begin using application intelligence to deliver the kind of insight you need. Application intelligence is simply detailed application level information about your network.

Acquiring this type of information can be difficult, unless you have implemented a visibility architecture with a network packet broker (NPB). A good NPB can filter network data based upon Layer 7 (application level) information which makes the process easy. Really good NPBs will provide additional meta data information like NetFlow data, geolocation information, device type, browser type, etc. With all of this information, you can really start to see what is happening on your network and where it's happening.

For instance, here are just a few cool things application intelligence can help you with:

■ Generate an application level dashboard to observe applications in use and bandwidth consumption on a per app basis

■ Troubleshoot localized and global network issues faster

■ Filter data to security and monitoring tools based upon application signatures to improve tool efficiency and speed of analysis

■ Identify bandwidth hogs and bandwidth explosions on the network, e.g., Smartphone apps

■ Use geolocation to show overloaded / underperforming network segments

■ Spot indicators of compromise on your network

■ Improve your adherence to regulatory compliance mandates

Application intelligence gives you summary information about how your network is performing. This includes a dashboard that shows you visually a list of applications in use, the percentage of network bandwidth allocation per application, a listing and breakdown of usage of device types and browser types, and the loading across your network.

The dashboard should also let you filter on one or more applications so that you can narrow the dashboard view to see only what you want and need to see. This dashboard will be a key factor in converting data into usable information because lets you intuitively visualize the information.

As an example, a visibility architecture that uses application intelligence information can be used to capture critical information needed for the whole troubleshooting process. Filtering can be created to isolate specific applications that are being reported to have problems.

Geolocation capability can also be used to help quickly locate geographic outages and potentially narrow troubleshooting efforts to specific vendors that may be causing network disruptions. This reduces troubleshooting costs and improves customer Quality of Experience.

Eliminating inspection of ... low-risk data can make your IDS solution up to 35% more efficient

Another powerful use case for application intelligence is to use application filtering to improve security and monitoring tool efficiencies. Delivering the right information is critical because garbage in results in garbage out. For instance, by using application intelligence to screen traffic before it is sent to an intrusion detection system (IDS), information that may not require screening (e.g. voice and video) can be routed downstream and bypass IDS inspection. Eliminating inspection of this low-risk data can make your IDS solution up to 35% more efficient.

Application intelligence can be used to identify slow or underperforming applications or network segments. For instance, application information, flow data, and geographic information can be combined to show what applications are running on your network, how much bandwidth each application is using, and what the geographic usage is for the application(s). This allows you to isolate and filter traffic matching specific applications, geographies, keywords, and handset types to start root analysis work flows.

Another use case allows you to access empirical data to identify bandwidth usage, trending, and growth needs. This empirical data can then be used to proactively manage network resources and new equipment installations, accurately forecast expansions, and perform better budgeting for expansions. The data can then be exported to other applications, like a Splunk application or something, for long-term data collection and performance trending.

One example is a wireless carrier with smartphone users. A new app, like a multi-user scavenger hunt app, could be relatively small one minute and then could literally exploded 1,000, 10,000 or even 100,000 times in the amount of bandwidth consumption in just a few weeks time. Unplanned bandwidth explosions like this can severely impact the quality of service and quality of experience on the network.

The bandwidth explosion issue isn't just one for service providers either. Bring Your Own Device (BYOD) and the plethora of apps on today's smartphones can easily affect network bandwidth for small to medium businesses as well as enterprises. Bandwidth explosions can happen on the wired and wireless networks in a short amount of time. This makes it critical to be able to observe the network in real-time to understand what is happening.

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Improve Your Performance with Application Intelligence

Keith Bromley

If you are like most IT professionals, which I am sure you are, you are dealing with a lot issues.

Typical issues include:

■ Constantly changing security threats to your network

■ An internal and external emphasis on your customer quality of experience

■ A greater need to troubleshoot problems faster

These three requirements are forcing IT to acquire an even better insight and understanding of their network to maximize its performance. One thing you can do to accomplish these goals is to begin using application intelligence to deliver the kind of insight you need. Application intelligence is simply detailed application level information about your network.

Acquiring this type of information can be difficult, unless you have implemented a visibility architecture with a network packet broker (NPB). A good NPB can filter network data based upon Layer 7 (application level) information which makes the process easy. Really good NPBs will provide additional meta data information like NetFlow data, geolocation information, device type, browser type, etc. With all of this information, you can really start to see what is happening on your network and where it's happening.

For instance, here are just a few cool things application intelligence can help you with:

■ Generate an application level dashboard to observe applications in use and bandwidth consumption on a per app basis

■ Troubleshoot localized and global network issues faster

■ Filter data to security and monitoring tools based upon application signatures to improve tool efficiency and speed of analysis

■ Identify bandwidth hogs and bandwidth explosions on the network, e.g., Smartphone apps

■ Use geolocation to show overloaded / underperforming network segments

■ Spot indicators of compromise on your network

■ Improve your adherence to regulatory compliance mandates

Application intelligence gives you summary information about how your network is performing. This includes a dashboard that shows you visually a list of applications in use, the percentage of network bandwidth allocation per application, a listing and breakdown of usage of device types and browser types, and the loading across your network.

The dashboard should also let you filter on one or more applications so that you can narrow the dashboard view to see only what you want and need to see. This dashboard will be a key factor in converting data into usable information because lets you intuitively visualize the information.

As an example, a visibility architecture that uses application intelligence information can be used to capture critical information needed for the whole troubleshooting process. Filtering can be created to isolate specific applications that are being reported to have problems.

Geolocation capability can also be used to help quickly locate geographic outages and potentially narrow troubleshooting efforts to specific vendors that may be causing network disruptions. This reduces troubleshooting costs and improves customer Quality of Experience.

Eliminating inspection of ... low-risk data can make your IDS solution up to 35% more efficient

Another powerful use case for application intelligence is to use application filtering to improve security and monitoring tool efficiencies. Delivering the right information is critical because garbage in results in garbage out. For instance, by using application intelligence to screen traffic before it is sent to an intrusion detection system (IDS), information that may not require screening (e.g. voice and video) can be routed downstream and bypass IDS inspection. Eliminating inspection of this low-risk data can make your IDS solution up to 35% more efficient.

Application intelligence can be used to identify slow or underperforming applications or network segments. For instance, application information, flow data, and geographic information can be combined to show what applications are running on your network, how much bandwidth each application is using, and what the geographic usage is for the application(s). This allows you to isolate and filter traffic matching specific applications, geographies, keywords, and handset types to start root analysis work flows.

Another use case allows you to access empirical data to identify bandwidth usage, trending, and growth needs. This empirical data can then be used to proactively manage network resources and new equipment installations, accurately forecast expansions, and perform better budgeting for expansions. The data can then be exported to other applications, like a Splunk application or something, for long-term data collection and performance trending.

One example is a wireless carrier with smartphone users. A new app, like a multi-user scavenger hunt app, could be relatively small one minute and then could literally exploded 1,000, 10,000 or even 100,000 times in the amount of bandwidth consumption in just a few weeks time. Unplanned bandwidth explosions like this can severely impact the quality of service and quality of experience on the network.

The bandwidth explosion issue isn't just one for service providers either. Bring Your Own Device (BYOD) and the plethora of apps on today's smartphones can easily affect network bandwidth for small to medium businesses as well as enterprises. Bandwidth explosions can happen on the wired and wireless networks in a short amount of time. This makes it critical to be able to observe the network in real-time to understand what is happening.

Related Links

Hot Topics

Related Vendors

The Latest

When AI Becomes the Corporate OS

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 ...

The $600 Billion Wake Up Call

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 ...

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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 ...

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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 ...

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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 ...

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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 ...