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One Way to Improve Application Performance for Higher Education

Keith Bromley

The education sector is undergoing significant change. National enrollment for higher education has declined 3 percent across the United States since 2015, according to the 2017 National Student Clearinghouse Research Center annual study. Decreased student enrollment is affecting many Higher education institutions and resulting in increased competition, student demands for more cutting-edge facilities, and shrinking profit margins, even as course fees reach all-time highs.

In one effort to respond to decreased admissions, many colleges and universities are focusing on a student-centered experience. For example, in addition to traditional classrooms, distance learning and mobile device support are being introduced to attract more millennials and older students. According to the 2017 Education infographic by Livestream, 77 percent of colleges offer online courses and 55 percent of college presidents predict that by 2022, all students will take at least some of their classes online.

However, implementing this new digital technology is not enough. Colleges and universities also need to ensure appropriate network and application performance for their remote distance learning feeds. Since live stream and on-demand video feeds are critically important, performance issues cannot be tolerated. This is a prime concern for both universities and K-12 institutions.

The key ingredient to optimum network and application performance is monitoring. You have to know what the network and its applications are, or are not, doing. Network visibility equipment, like taps and network packet brokers, can help.

Here is a list of clear visibility actions that can be implemented to address performance issues:

■ Remove on-premises blind spots – This is accomplished just by installing taps and network packet brokers (NPBs). These two components give you network performance monitoring (NPM) data access across your whole network.

■ Identify and document your network latency, on a per segment (as needed) basis – A proactive monitoring solution can be used to characterize your network. By actively testing between probes placed throughout your network, you can identify the latency, throughput, and quality of service across your whole network or just parts of it. This further allows you to understand performance issues and pinpoint where they occur.

■ Perform root cause analysis and debugging of performance issues – Once the taps and a packet brokers are installed, network data can be captured and sent to application performance monitoring (APM) tools for performance problem correlation and analysis to make sure that you maintain student quality of experience (QOE).

■ Remove cloud blind spots – Just as with the on-premises blind spot situation, cloud-based online education feeds can have issues as well. In this situation, a cloud visibility solution can be installed to collect packet-based data for those cloud apps and then the data can be passed on to an APM tool for analysis and QOE optimization.

■ Actively test network performance – When new applications are added to the network, this can affect network performance. A synthetic traffic generator allows you to respond to complaints about application or network slowness by actively testing different segments within your network to isolate and remediate problems faster. It can also be used to test software updates before they go live.

The diagram below illustrates how these technologies can be inserted into a generic education network.


The key point to remember is that acquiring the right monitoring data is the most important ingredient to creating and maintaining network and application performance.

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One Way to Improve Application Performance for Higher Education

Keith Bromley

The education sector is undergoing significant change. National enrollment for higher education has declined 3 percent across the United States since 2015, according to the 2017 National Student Clearinghouse Research Center annual study. Decreased student enrollment is affecting many Higher education institutions and resulting in increased competition, student demands for more cutting-edge facilities, and shrinking profit margins, even as course fees reach all-time highs.

In one effort to respond to decreased admissions, many colleges and universities are focusing on a student-centered experience. For example, in addition to traditional classrooms, distance learning and mobile device support are being introduced to attract more millennials and older students. According to the 2017 Education infographic by Livestream, 77 percent of colleges offer online courses and 55 percent of college presidents predict that by 2022, all students will take at least some of their classes online.

However, implementing this new digital technology is not enough. Colleges and universities also need to ensure appropriate network and application performance for their remote distance learning feeds. Since live stream and on-demand video feeds are critically important, performance issues cannot be tolerated. This is a prime concern for both universities and K-12 institutions.

The key ingredient to optimum network and application performance is monitoring. You have to know what the network and its applications are, or are not, doing. Network visibility equipment, like taps and network packet brokers, can help.

Here is a list of clear visibility actions that can be implemented to address performance issues:

■ Remove on-premises blind spots – This is accomplished just by installing taps and network packet brokers (NPBs). These two components give you network performance monitoring (NPM) data access across your whole network.

■ Identify and document your network latency, on a per segment (as needed) basis – A proactive monitoring solution can be used to characterize your network. By actively testing between probes placed throughout your network, you can identify the latency, throughput, and quality of service across your whole network or just parts of it. This further allows you to understand performance issues and pinpoint where they occur.

■ Perform root cause analysis and debugging of performance issues – Once the taps and a packet brokers are installed, network data can be captured and sent to application performance monitoring (APM) tools for performance problem correlation and analysis to make sure that you maintain student quality of experience (QOE).

■ Remove cloud blind spots – Just as with the on-premises blind spot situation, cloud-based online education feeds can have issues as well. In this situation, a cloud visibility solution can be installed to collect packet-based data for those cloud apps and then the data can be passed on to an APM tool for analysis and QOE optimization.

■ Actively test network performance – When new applications are added to the network, this can affect network performance. A synthetic traffic generator allows you to respond to complaints about application or network slowness by actively testing different segments within your network to isolate and remediate problems faster. It can also be used to test software updates before they go live.

The diagram below illustrates how these technologies can be inserted into a generic education network.


The key point to remember is that acquiring the right monitoring data is the most important ingredient to creating and maintaining network and application performance.

Hot Topics

The Latest

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 ...

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