Skip to main content

Cloud Performance Monitoring - Lift & Shift Doesn't Work

Keith Bromley

With all of the hype around cloud computing these days, it's a wonder that IT departments haven't come to an absolute standstill due to a bewildering amount of confusion. Don't get me wrong, cloud computing has clear and definite benefits. At the same time there is an excessive amount of vendor hype that it will fix a lot of problems which it will not. It can also create new problems with a lack of visibility and many IT professionals are disappointed with their leap to a pure cloud environment.

9 out of 10 respondents have seen a direct negative business impact due to lack of visibility into public cloud traffic

Consider this. A survey performed by Dimensional Research for Ixia showed that 9 out of 10 respondents have seen a direct negative business impact due to lack of visibility into public cloud traffic. This includes application and network troubleshooting and performance issues, as well as delays in resolving security alerts stemming from a lack of visibility.

In addition, Sanjit Ganguli of Gartner Research also conducted polling on public cloud migrations at the Gartner December 2017 Data Center Conference and found that 62 percent were not satisfied with the monitoring data they get from their cloud vendor now that they have moved to the cloud. In addition, 53 percent actually said that they were blind to what happens in their cloud network.

While not all cloud migration problems are avoidable, many can be. Specifically, performance issues are a real consideration for new cloud networks. Once you migrate to the cloud, and during the migration process, you will not have clear network performance data within your environment. It is up to you to implement this, if you want this visibility. The tools that the public cloud vendors provide will not be good enough.

Business intelligence applications are one example of a problem area. After porting the service from your completely controllable on-premises environment to a public cloud instance, you may find that it runs slower (after you receive multiple customer complaints). The "lift and shift" concept failed. The result is often an increase in more CPU, RAM, and interconnect bandwidth. This creates an unplanned and perpetual cost increase.

Another example is that you cannot natively tell how your applications are truly performing or even how your cloud instance is performing. Is it meeting or exceeding the service level agreement (SLA) that was put in place? Your cloud vendor will probably tell you that it is, but you have no independent data for a "check and balance" strategy on what they are delivering.

So, does this mean you give up using the cloud, hopefully not. There are clear business benefits to the cloud and to prolonged hybrid cloud solutions. The answer is to do a thorough assessment of what you are migrating and then perform baseline performance monitoring before, during, and after the move.

For instance, during the migration process, proactive performance monitoring of both your on-premises and cloud environments will be useful. Test the performance yourself to characterize how it is actually working at all phases. With the right tool, this testing can be fairly painless. An alternative is to copy and export cloud data back to your on-premises performance monitoring tools (assuming that you are operating a hybrid cloud environment) for analysis there. Many organizations that just blindly port services and applications to the cloud encounter cloud computing issues quickly, particularly performance issues.

Hot Topics

The Latest

For years, production operations teams have treated alert fatigue as a quality-of-life problem: something that makes on-call rotations miserable but isn't considered a direct contributor to outages. That framing doesn't capture how these systems fail, and we now have data to show why. More importantly, it's now clear alert fatigue is a symptom of a deeper issue: production systems have outgrown the current operational approaches ...

I was on a customer call last fall when an enterprise architect said something I haven't been able to shake. Her team had just spent four months trying to swap one AI vendor for another. The original plan said three weeks. "We didn't switch vendors," she told me. "We rebuilt half our integrations and discovered what we'd actually been depending on." Most enterprise leaders don't expect that to be the experience ...

Ask any senior SRE or platform engineer what keeps them up at night, and the answer probably isn't the monitoring tool — it's the data feeding it. The proliferation of APM, observability, and AIOps platforms has created a telemetry sprawl problem that most teams manage reactively rather than architect proactively. Metrics are going to one platform. Traces routed somewhere else. Logs duplicated across multiple backends because nobody wants to be caught without them when something breaks. Every redundant stream costs money ...

80% of respondents agree that the IT role is shifting from operators to orchestrators, according to the 2026 IT Trends Report: The Human Side of Autonomous IT from SolarWinds ...

40% of organizations deploying AI will implement dedicated AI observability tools by 2028 to monitor model performance, bias and outputs, according to Gartner ...

Until AI-powered engineering tools have live visibility of how code behaves at runtime, they cannot be trusted to autonomously ensure reliable systems, according to the State of AI-Powered Engineering Report 2026 report from Lightrun. The report reveals that a major volume of manual work is required when AI-generated code is deployed: 43% of AI-generated code requires manual debugging in production, even after passing QA or staging tests. Furthermore, an average of three manual redeploy cycles are required to verify a single AI-suggested code fix in production ...

Many organizations describe AI as strategic, but they do not manage it strategically. When AI plans are disconnected from strategy, detached from organizational learning, and protected from serious assumptions testing, the problem is no longer technical immaturity; it is a failure of management discipline ... Executives too often tell organizations to "use AI" before they define what AI is supposed to change. The problem deepens in organizations where strategy isn't well articulated in the first place ...

Across the enterprise technology landscape, a quiet crisis is playing out. Organizations have run hundreds, sometimes thousands, of generative AI pilots. Leadership has celebrated the proof of concept (POCs) ... Industry experience points to a sobering reality: only 5-10% of AI POCs that progress to the pilot stage successfully reach scaled production. The remaining 90% fail because the enterprise environment around them was never ready to absorb them, not the AI models ...

Today's modern systems are not what they once were. Organizations now rely on distributed systems, event-driven workflows, hybrid and multi-cloud environments and continuous delivery pipelines. While each adds flexibility, it also introduces new, often invisible failures. Development speed is no longer the primary bottleneck of innovation. Reliability is ...

Seeing is believing, or in this case, seeing is understanding, according to New Relic's 2025 Observability Forecast for Retail and eCommerce report. Retailers who want to provide exceptional customer experiences while improving IT operations efficiency are leaning on observability ... Here are five key takeaways from the report ...

Cloud Performance Monitoring - Lift & Shift Doesn't Work

Keith Bromley

With all of the hype around cloud computing these days, it's a wonder that IT departments haven't come to an absolute standstill due to a bewildering amount of confusion. Don't get me wrong, cloud computing has clear and definite benefits. At the same time there is an excessive amount of vendor hype that it will fix a lot of problems which it will not. It can also create new problems with a lack of visibility and many IT professionals are disappointed with their leap to a pure cloud environment.

9 out of 10 respondents have seen a direct negative business impact due to lack of visibility into public cloud traffic

Consider this. A survey performed by Dimensional Research for Ixia showed that 9 out of 10 respondents have seen a direct negative business impact due to lack of visibility into public cloud traffic. This includes application and network troubleshooting and performance issues, as well as delays in resolving security alerts stemming from a lack of visibility.

In addition, Sanjit Ganguli of Gartner Research also conducted polling on public cloud migrations at the Gartner December 2017 Data Center Conference and found that 62 percent were not satisfied with the monitoring data they get from their cloud vendor now that they have moved to the cloud. In addition, 53 percent actually said that they were blind to what happens in their cloud network.

While not all cloud migration problems are avoidable, many can be. Specifically, performance issues are a real consideration for new cloud networks. Once you migrate to the cloud, and during the migration process, you will not have clear network performance data within your environment. It is up to you to implement this, if you want this visibility. The tools that the public cloud vendors provide will not be good enough.

Business intelligence applications are one example of a problem area. After porting the service from your completely controllable on-premises environment to a public cloud instance, you may find that it runs slower (after you receive multiple customer complaints). The "lift and shift" concept failed. The result is often an increase in more CPU, RAM, and interconnect bandwidth. This creates an unplanned and perpetual cost increase.

Another example is that you cannot natively tell how your applications are truly performing or even how your cloud instance is performing. Is it meeting or exceeding the service level agreement (SLA) that was put in place? Your cloud vendor will probably tell you that it is, but you have no independent data for a "check and balance" strategy on what they are delivering.

So, does this mean you give up using the cloud, hopefully not. There are clear business benefits to the cloud and to prolonged hybrid cloud solutions. The answer is to do a thorough assessment of what you are migrating and then perform baseline performance monitoring before, during, and after the move.

For instance, during the migration process, proactive performance monitoring of both your on-premises and cloud environments will be useful. Test the performance yourself to characterize how it is actually working at all phases. With the right tool, this testing can be fairly painless. An alternative is to copy and export cloud data back to your on-premises performance monitoring tools (assuming that you are operating a hybrid cloud environment) for analysis there. Many organizations that just blindly port services and applications to the cloud encounter cloud computing issues quickly, particularly performance issues.

Hot Topics

The Latest

For years, production operations teams have treated alert fatigue as a quality-of-life problem: something that makes on-call rotations miserable but isn't considered a direct contributor to outages. That framing doesn't capture how these systems fail, and we now have data to show why. More importantly, it's now clear alert fatigue is a symptom of a deeper issue: production systems have outgrown the current operational approaches ...

I was on a customer call last fall when an enterprise architect said something I haven't been able to shake. Her team had just spent four months trying to swap one AI vendor for another. The original plan said three weeks. "We didn't switch vendors," she told me. "We rebuilt half our integrations and discovered what we'd actually been depending on." Most enterprise leaders don't expect that to be the experience ...

Ask any senior SRE or platform engineer what keeps them up at night, and the answer probably isn't the monitoring tool — it's the data feeding it. The proliferation of APM, observability, and AIOps platforms has created a telemetry sprawl problem that most teams manage reactively rather than architect proactively. Metrics are going to one platform. Traces routed somewhere else. Logs duplicated across multiple backends because nobody wants to be caught without them when something breaks. Every redundant stream costs money ...

80% of respondents agree that the IT role is shifting from operators to orchestrators, according to the 2026 IT Trends Report: The Human Side of Autonomous IT from SolarWinds ...

40% of organizations deploying AI will implement dedicated AI observability tools by 2028 to monitor model performance, bias and outputs, according to Gartner ...

Until AI-powered engineering tools have live visibility of how code behaves at runtime, they cannot be trusted to autonomously ensure reliable systems, according to the State of AI-Powered Engineering Report 2026 report from Lightrun. The report reveals that a major volume of manual work is required when AI-generated code is deployed: 43% of AI-generated code requires manual debugging in production, even after passing QA or staging tests. Furthermore, an average of three manual redeploy cycles are required to verify a single AI-suggested code fix in production ...

Many organizations describe AI as strategic, but they do not manage it strategically. When AI plans are disconnected from strategy, detached from organizational learning, and protected from serious assumptions testing, the problem is no longer technical immaturity; it is a failure of management discipline ... Executives too often tell organizations to "use AI" before they define what AI is supposed to change. The problem deepens in organizations where strategy isn't well articulated in the first place ...

Across the enterprise technology landscape, a quiet crisis is playing out. Organizations have run hundreds, sometimes thousands, of generative AI pilots. Leadership has celebrated the proof of concept (POCs) ... Industry experience points to a sobering reality: only 5-10% of AI POCs that progress to the pilot stage successfully reach scaled production. The remaining 90% fail because the enterprise environment around them was never ready to absorb them, not the AI models ...

Today's modern systems are not what they once were. Organizations now rely on distributed systems, event-driven workflows, hybrid and multi-cloud environments and continuous delivery pipelines. While each adds flexibility, it also introduces new, often invisible failures. Development speed is no longer the primary bottleneck of innovation. Reliability is ...

Seeing is believing, or in this case, seeing is understanding, according to New Relic's 2025 Observability Forecast for Retail and eCommerce report. Retailers who want to provide exceptional customer experiences while improving IT operations efficiency are leaning on observability ... Here are five key takeaways from the report ...