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Assuring Exceptional Experiences with Applications Requires Assuring Network Performance - Part 2

Nadeem Zahid
cPacket Networks

This is Part 2 of a blog series on how to find root cause of the most common application experience issues.

Start with: Assuring Exceptional Experiences with Applications Requires Assuring Network Performance - Part 1

Responsiveness Issues

This type of issue is often reported as "the application is too slow." A likely root cause of unacceptable responsiveness resulting from network issues is an overloaded network (e.g., the capacity of the network is insufficient to handle the current traffic). If a network is overloaded, it is possible that its DNS server is also overloaded and either responds very slowly or not at all. Observing traffic bursts, especially microbursts, with detailed metrics is another indicator of an overloaded network and a cause of irregular latencies. If any of these are the root cause, then traffic must be shaped accordingly and/or capacity must be added.

When resolving these issues, IT teams analyze network, application and protocol latency using observed metrics such as DNS and HTTP latency, one-way latency, round-trip time, and Zero-Window activity. Additional observed behaviors and metrics will reveal which specific problem is the culprit. These metrics include throughput measured as gigabits per second (Gbps), the number of connections per second, and the number of concurrent connections. Network packet and flow data provides the insights and context to identify the root cause. Packet data captured with high fidelity using high-performance monitoring will detect and characterize traffic bursts and the number of connections per second. Flow data reveals top talkers and the number of packets transmitted per second.

Streaming Issues

Communications and streaming applications that use Voice over IP (VoIP), videoconferencing, and other streaming services are increasingly in use for entertainment, education and collaboration, especially in the COVID-19 era. Experiences with these applications are directly impacted by network performance.

Choppy and freezing video, unsynchronized audio and video, audio dropout, and other noticeable types of distortion are the typical issues that result in unsatisfactory experiences. These annoying issues are the result of streaming errors and packet loss that are readily noticed, complained about, and reported to IT and customer support help desks.

To diagnose the root causes and assure exceptional streaming experiences, IT needs to monitor and observe jitter, sequence errors, retransmissions, and Maximum Transmission Unit (MTU) fragmentation. Excessive jitter and sequence errors result from various streaming errors, while retransmissions and fragmentation indicate the packet loss as the culprit. It is necessary to dig further to determine whether these problems are caused by routing problems or MTU fragmentation. High MTU values mean that larger packets are transmitted that take relatively longer to process and retransmit and hence inhibit a smooth flow of digitized voice and video streams.

Other Performance Issues

The applications that rely on streaming services such as high frequency trading and high-performance computing, are increasingly relying on higher throughput that is driving the use of 100Gbps connectivity. Timing tolerances, latencies and all other performance metrics become finer as data rates increase. This necessitates higher fidelity monitoring to provide the necessary visibility and observability to ensure the best possible SLEs and MTTR. As an example, detecting gaps in high frequency trading streams requires observing microbursts and latencies with sub-millisecond resolution. Therefore, it is essential to have a clearly defined SLE, especially for high-performance applications and underlying infrastructure, then match to it the metrics to observe and the tools and resolution needed to do so.

Experiences impact organizations in many ways, which is why delivering exceptional experiences is a critical success factor. Experiences with applications depend on network performance. As a result, effectively and efficiently assuring experiences requires visibility and observability into both network and application behaviors and metrics. Network Performance Management and Diagnostics driven by monitoring is therefore a necessary complement to Application Performance Management in all environments.

Nadeem Zahid is VP of Product Management & Marketing at cPacket Networks

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Assuring Exceptional Experiences with Applications Requires Assuring Network Performance - Part 2

Nadeem Zahid
cPacket Networks

This is Part 2 of a blog series on how to find root cause of the most common application experience issues.

Start with: Assuring Exceptional Experiences with Applications Requires Assuring Network Performance - Part 1

Responsiveness Issues

This type of issue is often reported as "the application is too slow." A likely root cause of unacceptable responsiveness resulting from network issues is an overloaded network (e.g., the capacity of the network is insufficient to handle the current traffic). If a network is overloaded, it is possible that its DNS server is also overloaded and either responds very slowly or not at all. Observing traffic bursts, especially microbursts, with detailed metrics is another indicator of an overloaded network and a cause of irregular latencies. If any of these are the root cause, then traffic must be shaped accordingly and/or capacity must be added.

When resolving these issues, IT teams analyze network, application and protocol latency using observed metrics such as DNS and HTTP latency, one-way latency, round-trip time, and Zero-Window activity. Additional observed behaviors and metrics will reveal which specific problem is the culprit. These metrics include throughput measured as gigabits per second (Gbps), the number of connections per second, and the number of concurrent connections. Network packet and flow data provides the insights and context to identify the root cause. Packet data captured with high fidelity using high-performance monitoring will detect and characterize traffic bursts and the number of connections per second. Flow data reveals top talkers and the number of packets transmitted per second.

Streaming Issues

Communications and streaming applications that use Voice over IP (VoIP), videoconferencing, and other streaming services are increasingly in use for entertainment, education and collaboration, especially in the COVID-19 era. Experiences with these applications are directly impacted by network performance.

Choppy and freezing video, unsynchronized audio and video, audio dropout, and other noticeable types of distortion are the typical issues that result in unsatisfactory experiences. These annoying issues are the result of streaming errors and packet loss that are readily noticed, complained about, and reported to IT and customer support help desks.

To diagnose the root causes and assure exceptional streaming experiences, IT needs to monitor and observe jitter, sequence errors, retransmissions, and Maximum Transmission Unit (MTU) fragmentation. Excessive jitter and sequence errors result from various streaming errors, while retransmissions and fragmentation indicate the packet loss as the culprit. It is necessary to dig further to determine whether these problems are caused by routing problems or MTU fragmentation. High MTU values mean that larger packets are transmitted that take relatively longer to process and retransmit and hence inhibit a smooth flow of digitized voice and video streams.

Other Performance Issues

The applications that rely on streaming services such as high frequency trading and high-performance computing, are increasingly relying on higher throughput that is driving the use of 100Gbps connectivity. Timing tolerances, latencies and all other performance metrics become finer as data rates increase. This necessitates higher fidelity monitoring to provide the necessary visibility and observability to ensure the best possible SLEs and MTTR. As an example, detecting gaps in high frequency trading streams requires observing microbursts and latencies with sub-millisecond resolution. Therefore, it is essential to have a clearly defined SLE, especially for high-performance applications and underlying infrastructure, then match to it the metrics to observe and the tools and resolution needed to do so.

Experiences impact organizations in many ways, which is why delivering exceptional experiences is a critical success factor. Experiences with applications depend on network performance. As a result, effectively and efficiently assuring experiences requires visibility and observability into both network and application behaviors and metrics. Network Performance Management and Diagnostics driven by monitoring is therefore a necessary complement to Application Performance Management in all environments.

Nadeem Zahid is VP of Product Management & Marketing at cPacket Networks

Hot Topics

The Latest

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 ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...