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One Trick To Creating Better and Faster Rollouts

Keith Bromley

If you have not been engaging in proactive monitoring efforts, it’s something you might want to consider. Most IT monitoring efforts are reactive — you either periodically try some basic attempts to find problems on the live network or you decide to wait and respond to customer complaints. This is supported by the EMA Network Management Megatrends 2016 report which states that approximately 40% of network problems are detected and reported by end users.

Everyone in IT understands this. It’s unfortunate but true — you can’t be everywhere doing everything for everyone. In addition, the report states that 26% of the respondents reported that one of their top networking challenges is the lack of end-to-end, multisite network visibility and troubleshooting capabilities. This is where visibility technology can help by giving you access to critical monitoring, when you need it and in the format you need it.

But improved visibility isn’t usually enough. You are probably going to need a more proactive troubleshooting approach as well. Proactive monitoring uses visibility technology to actively test your solution either before rollout, during rollout, or after rollout. For instance, it can be used to provide better and faster network and application rollouts by pre-testing the network with synthetic traffic to understand how the solution will perform against either specific application traffic or a combination of traffic types. The synthetic traffic provides you the network and/or application loading of a “busy hour” and the flexibility to perform evaluations during the network maintenance window.

Proactive monitoring has several fundamental benefits including the ability to:

■ Know the performance level of your network immediately

■ Understand how well your applications are running

■ Validate SLAs — both on-premises and in the cloud

■ Test upgrades during maintenance windows before company employees do

Network performance and application performance testing may sound simple, but these can actually be difficult to ascertain. To get a true indication of network performance, the network needs to have a large amount of traffic on it, which makes you dependent upon peak busy hours. This type of solution allows you to place probes anywhere in your network and test whenever you want to. It also allows you to accurately simulate the right type of traffic so that Application Performance Management (APM) tools can observe how well applications are truly performing. For instance, this allows you to simulate small packets or Skype-like data if you want to test your instant message (IM)/voice/video solution.

Once you’ve conducted your proactive monitoring test cases, you’ll have the information you need to either continue with your solution update (i.e. continue with the network or application rollout) or perform a rollback (before it affects any users outside of the maintenance window).

Proactive monitoring also allows you to perform SLA validation during business hours, since it is not service disrupting. This allows you validate the SLA performance at will. The information gathered can then be used to inform management about which goals are being met. If goals are not being met, you can use the impartial data you have collected and contact your vendor to have them either fix any observed network problems, or give you a discount if they are failing to meet agreed upon SLAs.

The final benefit is that there are proactive monitoring solutions on the market that let you test your on-premises solution as well as your cloud solution. This can be especially important if you have a hybrid solution right now, and are in the (often multi-year) process of transitioning from the physical to the virtual (cloud) world. A proactive monitoring testing and monitoring approach gives you the confidence that your application rollouts will be successful in either network.

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One Trick To Creating Better and Faster Rollouts

Keith Bromley

If you have not been engaging in proactive monitoring efforts, it’s something you might want to consider. Most IT monitoring efforts are reactive — you either periodically try some basic attempts to find problems on the live network or you decide to wait and respond to customer complaints. This is supported by the EMA Network Management Megatrends 2016 report which states that approximately 40% of network problems are detected and reported by end users.

Everyone in IT understands this. It’s unfortunate but true — you can’t be everywhere doing everything for everyone. In addition, the report states that 26% of the respondents reported that one of their top networking challenges is the lack of end-to-end, multisite network visibility and troubleshooting capabilities. This is where visibility technology can help by giving you access to critical monitoring, when you need it and in the format you need it.

But improved visibility isn’t usually enough. You are probably going to need a more proactive troubleshooting approach as well. Proactive monitoring uses visibility technology to actively test your solution either before rollout, during rollout, or after rollout. For instance, it can be used to provide better and faster network and application rollouts by pre-testing the network with synthetic traffic to understand how the solution will perform against either specific application traffic or a combination of traffic types. The synthetic traffic provides you the network and/or application loading of a “busy hour” and the flexibility to perform evaluations during the network maintenance window.

Proactive monitoring has several fundamental benefits including the ability to:

■ Know the performance level of your network immediately

■ Understand how well your applications are running

■ Validate SLAs — both on-premises and in the cloud

■ Test upgrades during maintenance windows before company employees do

Network performance and application performance testing may sound simple, but these can actually be difficult to ascertain. To get a true indication of network performance, the network needs to have a large amount of traffic on it, which makes you dependent upon peak busy hours. This type of solution allows you to place probes anywhere in your network and test whenever you want to. It also allows you to accurately simulate the right type of traffic so that Application Performance Management (APM) tools can observe how well applications are truly performing. For instance, this allows you to simulate small packets or Skype-like data if you want to test your instant message (IM)/voice/video solution.

Once you’ve conducted your proactive monitoring test cases, you’ll have the information you need to either continue with your solution update (i.e. continue with the network or application rollout) or perform a rollback (before it affects any users outside of the maintenance window).

Proactive monitoring also allows you to perform SLA validation during business hours, since it is not service disrupting. This allows you validate the SLA performance at will. The information gathered can then be used to inform management about which goals are being met. If goals are not being met, you can use the impartial data you have collected and contact your vendor to have them either fix any observed network problems, or give you a discount if they are failing to meet agreed upon SLAs.

The final benefit is that there are proactive monitoring solutions on the market that let you test your on-premises solution as well as your cloud solution. This can be especially important if you have a hybrid solution right now, and are in the (often multi-year) process of transitioning from the physical to the virtual (cloud) world. A proactive monitoring testing and monitoring approach gives you the confidence that your application rollouts will be successful in either network.

Hot Topics

The Latest

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...

The gap is widening between what teams spend on observability tools and the value they receive amid surging data volumes and budget pressures, according to The Breaking Point for Observability Leaders, a report from Imply ...

Seamless shopping is a basic demand of today's boundaryless consumer — one with little patience for friction, limited tolerance for disconnected experiences and minimal hesitation in switching brands. Customers expect intuitive, highly personalized experiences and the ability to move effortlessly across physical and digital channels within the same journey. Failure to deliver can cost dearly ...

If your best engineers spend their days sorting tickets and resetting access, you are wasting talent. New global data shows that employees in the IT sector rank among the least motivated across industries. They're under a lot of pressure from many angles. Pressure to upskill and uncertainty around what agentic AI means for job security is creating anxiety. Meanwhile, these roles often function like an on-call job and require many repetitive tasks ...