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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.

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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.

Hot Topics

The Latest

E-commerce is set to skyrocket with a 9% rise over the next few years ... To thrive in this competitive environment, retailers must identify digital resilience as their top priority. In a world where savvy shoppers expect 24/7 access to online deals and experiences, any unexpected downtime to digital services can lead to significant financial losses, damage to brand reputation, abandoned carts with designer shoes, and additional issues ...

Efficiency is a highly-desirable objective in business ... We're seeing this scenario play out in enterprises around the world as they continue to struggle with infrastructures and remote work models with an eye toward operational efficiencies. In contrast to that goal, a recent Broadcom survey of global IT and network professionals found widespread adoption of these strategies is making the network more complex and hampering observability, leading to uptime, performance and security issues. Let's look more closely at these challenges ...

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The 2025 Catchpoint SRE Report dives into the forces transforming the SRE landscape, exploring both the challenges and opportunities ahead. Let's break down the key findings and what they mean for SRE professionals and the businesses relying on them ...

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Catchpoint

The pressure on IT teams has never been greater. As data environments grow increasingly complex, resource shortages are emerging as a major obstacle for IT leaders striving to meet the demands of modern infrastructure management ... According to DataStrike's newly released 2025 Data Infrastructure Survey Report, more than half (54%) of IT leaders cite resource limitations as a top challenge, highlighting a growing trend toward outsourcing as a solution ...

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Gartner revealed its top strategic predictions for 2025 and beyond. Gartner's top predictions explore how generative AI (GenAI) is affecting areas where most would assume only humans can have lasting impact ...

The adoption of artificial intelligence (AI) is accelerating across the telecoms industry, with 88% of fixed broadband service providers now investigating or trialing AI automation to enhance their fixed broadband services, according to new research from Incognito Software Systems and Omdia ...

 

AWS is a cloud-based computing platform known for its reliability, scalability, and flexibility. However, as helpful as its comprehensive infrastructure is, disparate elements and numerous siloed components make it difficult for admins to visualize the cloud performance in detail. It requires meticulous monitoring techniques and deep visibility to understand cloud performance and analyze operational efficiency in detail to ensure seamless cloud operations ...

Imagine a future where software, once a complex obstacle, becomes a natural extension of daily workflow — an intuitive, seamless experience that maximizes productivity and efficiency. This future is no longer a distant vision but a reality being crafted by the transformative power of Artificial Intelligence ...

Enterprise data sprawl already challenges companies' ability to protect and back up their data. Much of this information is never fully secured, leaving organizations vulnerable. Now, as GenAI platforms emerge as yet another environment where enterprise data is consumed, transformed, and created, this fragmentation is set to intensify ...

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