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Seven Tips for Optimizing Network Performance - Part 2

Jay Botelho

Despite careful planning and monitoring, users still experience stuttering video calls, slow downloads, and dropped calls — all symptoms of common network problems. That's why proactive monitoring and optimization of the network is critical to keeping business operations running optimally. To help, let's look at some more network performance management tips that can keep your team ahead of the curve.

Start with Seven Tips for Optimizing Network Performance - Part 1

4. Update Software and Firmware

This is obviously critical for security, but when it comes to network performance, older software and firmware can also be a big problem. No one knows better than the manufacturer about the strengths, and weaknesses, of their products. Most products today, whether hardware or software, are essentially driven by the software and firmware that they run. Even though it may seem like the product you're using is stable, the "if it's not broken don't fix it" rule is not the optimal choice. Manufacturers know when there are underlying problems in their products that you may not see or may not be experiencing right now.

5. Establish a View of Network Topology

It's every network engineer's dream: a clear and concise dashboard that depicts the network topology from end to end. It sounds simple, but of course it's not. Network topologies take different forms, depending on the perspective of the user. But many agree that at least one depiction (that of each flow traversing the network, from client to server and back) is extremely useful for visualizing and troubleshooting network performance issues. The ability of solutions to provide this visualization is being taxed by many new technologies, including SD-WAN and cloud.

Make sure the network monitoring and visualization solution you choose can trace flows across and within all these different technologies. This is especially true for cloud since so much processing has already been pushed to the cloud, and the cloud infrastructure is very dynamic. It's imperative to track your network traffic not only to your cloud providers, but inside the cloud infrastructure whenever possible to retain the same level of troubleshooting you had when you hosted your applications in your own data center.

6. Implement Bandwidth-Friendly Policies

From the network engineering perspective these policies are bandwidth-friendly, but users may not see it the same way. Bandwidth is a commodity, and with most commodities users will use as much as they can if they see the commodity as free. And your corporate infrastructure users see bandwidth as being free and unlimited, even though we know that is far from the case. From a corporate perspective, bandwidth-friendly policies are those that allow business traffic to flow unimpeded on your network, but limit or perhaps even block traffic that is not essential.

Fortunately, there are ways to limit non-essential business traffic without blocking it entirely, keeping the user revolt at bay. This can be done through QoS settings, using traffic-shaping technologies, or taking advantage of SD-WAN features, assuming SD-WAN is already in use. The choice depends on the degree of control needed.

7. Use Automation When Possible

Automation is the holy grail in network performance management and includes finding a solution that monitors your network 24x7, detecting every problem before it happens, and adjusting the network to prevent the problem. But every network is different, and every situation is different, making true automation one of the most difficult areas to address in network management, never mind the blind trust required. But with the strides made in end-to-end network monitoring, along with the predictive capabilities of AI/ML solutions for detecting problems, the industry is moving forward. We can't expect automation in every area, and probably wouldn't trust automation in every area, so the best approach is to start small with technologies you can trust.

For example, relying on built-in automation between various solutions used in your network monitoring and management process. More specifically, integrating your network monitoring and trouble ticket systems such that critical alerts from network monitoring opens trouble tickets and begins feeding the system with key data so that network engineers can hit the ground running when they begin working on the issue.

Optimizing the network to ensure it meets the needs of users is becoming more and more complex. But the good news is that new tools and technologies are making it easier than ever to automate functionality, visualize performance and isolate problems before they become major issues for the business (not to mention providing tools for planning). Consider these tips when strategizing about your network monitoring and management to stay one step ahead of network problems.

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 ...

Seven Tips for Optimizing Network Performance - Part 2

Jay Botelho

Despite careful planning and monitoring, users still experience stuttering video calls, slow downloads, and dropped calls — all symptoms of common network problems. That's why proactive monitoring and optimization of the network is critical to keeping business operations running optimally. To help, let's look at some more network performance management tips that can keep your team ahead of the curve.

Start with Seven Tips for Optimizing Network Performance - Part 1

4. Update Software and Firmware

This is obviously critical for security, but when it comes to network performance, older software and firmware can also be a big problem. No one knows better than the manufacturer about the strengths, and weaknesses, of their products. Most products today, whether hardware or software, are essentially driven by the software and firmware that they run. Even though it may seem like the product you're using is stable, the "if it's not broken don't fix it" rule is not the optimal choice. Manufacturers know when there are underlying problems in their products that you may not see or may not be experiencing right now.

5. Establish a View of Network Topology

It's every network engineer's dream: a clear and concise dashboard that depicts the network topology from end to end. It sounds simple, but of course it's not. Network topologies take different forms, depending on the perspective of the user. But many agree that at least one depiction (that of each flow traversing the network, from client to server and back) is extremely useful for visualizing and troubleshooting network performance issues. The ability of solutions to provide this visualization is being taxed by many new technologies, including SD-WAN and cloud.

Make sure the network monitoring and visualization solution you choose can trace flows across and within all these different technologies. This is especially true for cloud since so much processing has already been pushed to the cloud, and the cloud infrastructure is very dynamic. It's imperative to track your network traffic not only to your cloud providers, but inside the cloud infrastructure whenever possible to retain the same level of troubleshooting you had when you hosted your applications in your own data center.

6. Implement Bandwidth-Friendly Policies

From the network engineering perspective these policies are bandwidth-friendly, but users may not see it the same way. Bandwidth is a commodity, and with most commodities users will use as much as they can if they see the commodity as free. And your corporate infrastructure users see bandwidth as being free and unlimited, even though we know that is far from the case. From a corporate perspective, bandwidth-friendly policies are those that allow business traffic to flow unimpeded on your network, but limit or perhaps even block traffic that is not essential.

Fortunately, there are ways to limit non-essential business traffic without blocking it entirely, keeping the user revolt at bay. This can be done through QoS settings, using traffic-shaping technologies, or taking advantage of SD-WAN features, assuming SD-WAN is already in use. The choice depends on the degree of control needed.

7. Use Automation When Possible

Automation is the holy grail in network performance management and includes finding a solution that monitors your network 24x7, detecting every problem before it happens, and adjusting the network to prevent the problem. But every network is different, and every situation is different, making true automation one of the most difficult areas to address in network management, never mind the blind trust required. But with the strides made in end-to-end network monitoring, along with the predictive capabilities of AI/ML solutions for detecting problems, the industry is moving forward. We can't expect automation in every area, and probably wouldn't trust automation in every area, so the best approach is to start small with technologies you can trust.

For example, relying on built-in automation between various solutions used in your network monitoring and management process. More specifically, integrating your network monitoring and trouble ticket systems such that critical alerts from network monitoring opens trouble tickets and begins feeding the system with key data so that network engineers can hit the ground running when they begin working on the issue.

Optimizing the network to ensure it meets the needs of users is becoming more and more complex. But the good news is that new tools and technologies are making it easier than ever to automate functionality, visualize performance and isolate problems before they become major issues for the business (not to mention providing tools for planning). Consider these tips when strategizing about your network monitoring and management to stay one step ahead of network problems.

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 ...