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How to Improve Cloud Computing with Performance Monitoring

Keith Bromley

According to a webinar presented by Viavi, 6 Steps for Maintaining Control in the Cloud, a survey was conducted by Gartner Research with IT engineers that had moved workloads to the cloud. The results showed that approximately 53% of respondents were blind as to what happens in their cloud network and 79% were dissatisfied with the monitoring data that they get about their cloud network. This lack of proper monitoring data leads to a lack of ability to accurately understand what your network is doing and how well it is/is not performing.

In a previous blog, I talked about how to get visibility into cloud networks and resolve the first part of the problem. This included why visibility was important and how to accomplish it. Once you have that information, the next thing you need to understand is the performance of your cloud network so that you can answer important questions. This includes:

How will the network handle the application data that you currently have?

Is the current contracted work space enough?

Will you encounter performance problems and need to upgrade the CPU and memory in a hurry before you get more user complaints?

Here are three suggestions to help you:

■ Test your cloud network for adequate capacity before you migrate from your current on-premises solution

■ Monitor your cloud and on-premises networks during the migration process

■ Continually verify that your cloud provider is delivering upon the contracted SLA

To get the answers you want, the first thing you will want to do is to insert virtual taps into your cloud network so that you get the proper monitoring data you need.

The second thing you will want to do is create a proactive cloud monitoring solution. Basically, this is a monitoring solution that uses software agents and probes that you can place across your cloud and physical infrastructure.

With a proactive monitoring solution, you can use visibility technology to actively test your solution before migration, during migration, and after migration. For instance, you can pre-test 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.

Once the migration starts, you can measure the ambient latency, throughput, and performance problems on a per-hop basis within the network to see how it is performing. This lets you analyze both 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 testing and monitoring approach gives you the confidence that your new application rollouts will be successful in either network.

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.

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How to Improve Cloud Computing with Performance Monitoring

Keith Bromley

According to a webinar presented by Viavi, 6 Steps for Maintaining Control in the Cloud, a survey was conducted by Gartner Research with IT engineers that had moved workloads to the cloud. The results showed that approximately 53% of respondents were blind as to what happens in their cloud network and 79% were dissatisfied with the monitoring data that they get about their cloud network. This lack of proper monitoring data leads to a lack of ability to accurately understand what your network is doing and how well it is/is not performing.

In a previous blog, I talked about how to get visibility into cloud networks and resolve the first part of the problem. This included why visibility was important and how to accomplish it. Once you have that information, the next thing you need to understand is the performance of your cloud network so that you can answer important questions. This includes:

How will the network handle the application data that you currently have?

Is the current contracted work space enough?

Will you encounter performance problems and need to upgrade the CPU and memory in a hurry before you get more user complaints?

Here are three suggestions to help you:

■ Test your cloud network for adequate capacity before you migrate from your current on-premises solution

■ Monitor your cloud and on-premises networks during the migration process

■ Continually verify that your cloud provider is delivering upon the contracted SLA

To get the answers you want, the first thing you will want to do is to insert virtual taps into your cloud network so that you get the proper monitoring data you need.

The second thing you will want to do is create a proactive cloud monitoring solution. Basically, this is a monitoring solution that uses software agents and probes that you can place across your cloud and physical infrastructure.

With a proactive monitoring solution, you can use visibility technology to actively test your solution before migration, during migration, and after migration. For instance, you can pre-test 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.

Once the migration starts, you can measure the ambient latency, throughput, and performance problems on a per-hop basis within the network to see how it is performing. This lets you analyze both 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 testing and monitoring approach gives you the confidence that your new application rollouts will be successful in either network.

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.

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 ...