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Maintaining Application Performance with Distributed Users

Nadeem Zahid
cPacket Networks

Thanks to pandemic-related work-from-home (WFH) and digital/mobile customer experience initiatives, employees and users are more distributed than ever. At the same time, organizations everywhere are adopting a cloud-first or cloud-smart architecture, distributing their business applications across private and public cloud infrastructures. Private data centers continue to be consolidated, while more and more branch offices are connecting to data centers and the public cloud simultaneously. Maintaining application performance for distributed users in this increasingly hybrid environment is a significant challenge for IT teams.

Application performance depends on network performance — networks connect end-users and IoT devices with applications and connect application components such as application servers, database servers and microservices together. Whether users are internal employees or external customers, their experience with enterprise and web-based and SaaS applications directly affect an organization's success, either through sales and revenue or employee productivity. Maintaining good application performance through network and application monitoring and troubleshooting helps the business stay on top of their mission-critical business applications to succeed.

IT faces many new challenges when trying to do this for a distributed user base, including:

No visibility into WFH and SaaS traffic: IT no longer has full visibility into traffic from users working from home or remote locations and using SaaS applications that cross the public internet. They'll be blind to any issues and forced to rely on user complaints to diagnose any problems — not a recipe for success.

Tapping the public cloud: The cloud is often a major blind spot to the Application Operations (AppOps) team. How can they measure, much less assure, application performance and dependencies for traffic they can't see? Cloud-native monitoring tools can help observe infrastructure and application layers, but they come with significant limitations. They are vendor-specific, often lack features and visibility compared to on-premises tools, and typically do not integrate well with those on-premises tools.

Troubleshooting without control: Remote employees might be working from a variety of locations — home, public networks, branch offices, or headquarters — and key applications may be virtualized, in the cloud, or located on premise. Traffic going between these many locations that does not pass through a physical switch or firewall and is invisible to traditional network traffic collection and analysis tools. The pressure on IT to ensure a good experience for users in all these scenarios has increased, but their control and ability to troubleshoot has gone down.

To ensure application performance for distributed users, IT must reliably monitor traffic across physical, virtual and cloud-native elements deployed across data centers, branch offices, and multi-cloud environments. Here are some techniques for accomplishing this:

Getting the Right Data

The first step toward ensuring application performance for distributed users is data mining. This starts with tapping strategic points in the network across physical, virtual and cloud infrastructure. IT must collect data from all critical locations including north-south traffic into and out of data centers and cloud as well as east-west traffic between virtual machines and/or application and database components of a software-defined data center. Speeds and feeds, scale, and cost matter at this stage. Then IT needs an analysis tool to make sense out of the accumulated packets, flow and metadata. This quickly gets complicated, but in general, IT should be able to measure baselines for application and network performance (latency and connection errors, for example), set thresholds for normal behavior, map dependencies, and generate alerts for service level monitoring. This last part is vital — alerting when performance deviates from a normal range allows IT to proactively investigate and fix issues before users complain.

Tapping the Cloud

One successful approach to collecting, consolidating, and analyzing traffic in the cloud involves a software-only solution natively integrated with leading Virtual Private Cloud (VPC) traffic-mirroring services. Advanced functions such as filtering, load balancing, slicing, etc. can be applied to the cloud application workloads. This not only enables seamless access to the VPC's network data, but it also reduces complexity and cost. By natively replicating and monitoring network traffic to tools within their VPC, IT teams can avoid using forwarding agents or container-based sensors.

By monitoring application traffic before a cloud migration, IT can build a baseline of normal performance. During and after the migration, they can continue monitoring to see if performance deviates, and proactively identify issues before they affect users.

Distributed user bases are here to stay, thanks to hybrid work schedules, cloud migrations, virtualization and data center consolidation. IT must adapt to this new reality and ensure their monitoring capabilities can proactively identify linked network and application issues and reduce cost and complexity no matter where users are located.

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

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Maintaining Application Performance with Distributed Users

Nadeem Zahid
cPacket Networks

Thanks to pandemic-related work-from-home (WFH) and digital/mobile customer experience initiatives, employees and users are more distributed than ever. At the same time, organizations everywhere are adopting a cloud-first or cloud-smart architecture, distributing their business applications across private and public cloud infrastructures. Private data centers continue to be consolidated, while more and more branch offices are connecting to data centers and the public cloud simultaneously. Maintaining application performance for distributed users in this increasingly hybrid environment is a significant challenge for IT teams.

Application performance depends on network performance — networks connect end-users and IoT devices with applications and connect application components such as application servers, database servers and microservices together. Whether users are internal employees or external customers, their experience with enterprise and web-based and SaaS applications directly affect an organization's success, either through sales and revenue or employee productivity. Maintaining good application performance through network and application monitoring and troubleshooting helps the business stay on top of their mission-critical business applications to succeed.

IT faces many new challenges when trying to do this for a distributed user base, including:

No visibility into WFH and SaaS traffic: IT no longer has full visibility into traffic from users working from home or remote locations and using SaaS applications that cross the public internet. They'll be blind to any issues and forced to rely on user complaints to diagnose any problems — not a recipe for success.

Tapping the public cloud: The cloud is often a major blind spot to the Application Operations (AppOps) team. How can they measure, much less assure, application performance and dependencies for traffic they can't see? Cloud-native monitoring tools can help observe infrastructure and application layers, but they come with significant limitations. They are vendor-specific, often lack features and visibility compared to on-premises tools, and typically do not integrate well with those on-premises tools.

Troubleshooting without control: Remote employees might be working from a variety of locations — home, public networks, branch offices, or headquarters — and key applications may be virtualized, in the cloud, or located on premise. Traffic going between these many locations that does not pass through a physical switch or firewall and is invisible to traditional network traffic collection and analysis tools. The pressure on IT to ensure a good experience for users in all these scenarios has increased, but their control and ability to troubleshoot has gone down.

To ensure application performance for distributed users, IT must reliably monitor traffic across physical, virtual and cloud-native elements deployed across data centers, branch offices, and multi-cloud environments. Here are some techniques for accomplishing this:

Getting the Right Data

The first step toward ensuring application performance for distributed users is data mining. This starts with tapping strategic points in the network across physical, virtual and cloud infrastructure. IT must collect data from all critical locations including north-south traffic into and out of data centers and cloud as well as east-west traffic between virtual machines and/or application and database components of a software-defined data center. Speeds and feeds, scale, and cost matter at this stage. Then IT needs an analysis tool to make sense out of the accumulated packets, flow and metadata. This quickly gets complicated, but in general, IT should be able to measure baselines for application and network performance (latency and connection errors, for example), set thresholds for normal behavior, map dependencies, and generate alerts for service level monitoring. This last part is vital — alerting when performance deviates from a normal range allows IT to proactively investigate and fix issues before users complain.

Tapping the Cloud

One successful approach to collecting, consolidating, and analyzing traffic in the cloud involves a software-only solution natively integrated with leading Virtual Private Cloud (VPC) traffic-mirroring services. Advanced functions such as filtering, load balancing, slicing, etc. can be applied to the cloud application workloads. This not only enables seamless access to the VPC's network data, but it also reduces complexity and cost. By natively replicating and monitoring network traffic to tools within their VPC, IT teams can avoid using forwarding agents or container-based sensors.

By monitoring application traffic before a cloud migration, IT can build a baseline of normal performance. During and after the migration, they can continue monitoring to see if performance deviates, and proactively identify issues before they affect users.

Distributed user bases are here to stay, thanks to hybrid work schedules, cloud migrations, virtualization and data center consolidation. IT must adapt to this new reality and ensure their monitoring capabilities can proactively identify linked network and application issues and reduce cost and complexity no matter where users are located.

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

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 ...