Skip to main content

Universal Monitoring Crimes and What to Do About Them - Part 2

Leon Adato

To help your organization increase data center efficiency and get the most benefit out of your monitoring solutions, here are the remaining universal monitoring crimes and what you can do about them:

Start with Universal Monitoring Crimes and What to Do About Them - Part 1

4. Flapping or sawtoothing alerts

When an alert repeatedly triggers (a device that keeps rebooting itself or processes keep deleting/creating temporary page files so that one moment it's over threshold, the next it's below, for example), that condition is known as flapping or sawtoothing.

What to do about it: These types of alerts have several possible resolutions based on what is supported by your monitoring solution and which best fits the specific situation:

■ GOOD: Suppress events within a window. Ignoring duplicated events within a certain period of time is often all you need to avoid meaningless duplicates.

■ ALSO GOOD: As mentioned previously, add a time delay to allow for self-resolution, avoid false-positives, and eliminate other potential issues that don't necessarily require a remediation response.

■ BETTER: Leverage "Reset" logic. Wait for a reset event before triggering a new alert of the same kind. Avoid making the reset logic merely the reverse of the trigger (if the alert is > 90%, the reset might be 90%). Instead, code the reset rules separately so that you might trigger when disk > 90% for 15 minutes, but it won't reset until it's 80% for 30.

■ BEST: Two-way communication with a ticket or alert management system. This is where the monitoring system communicates with the ticket and/or alert tracking system, so you can never cut the same alert for the same device until a human has actively corrected the original problem and closed the ticket.

5. No lab, test, or QA environments for your monitoring system

If your monitoring system is watching and alerting on mission-critical systems within the enterprise, then it is mission critical itself. But despite the fact that many organizations set up a proof-of-concept environment when evaluating monitoring solutions, once the production system is selected and rolled out, they fail to have any type of lab, test, or QA system that is maintained on an ongoing basis to help ensure the system is maintained.

What to do about it: Duh. Implement test, dev, and/or QA installations that serve to ensure your monitoring system has the oversight necessary for a mission-critical application.

■ TEST: An (often temporary) environment where patches and upgrades can be tested before attempting them in production.

■ DEV: An environment that mirrors production in terms of software, but where monitors for new equipment, applications, reports, or alerts can be set up and tested before rolling those solutions to production. And as mentioned earlier, this is the perfect place to also monitor your production monitoring environment.

■ QA: An environment that mirrors the previous version of production, so that if issues are found in production, they can be double-checked to confirm whether the problem was introduced in the last revision.

Note that I'm not implying you necessarily must have all three, but it's worth considering the value of at least one. Because "none" is a really bad choice.

Final thoughts

The rate of technical change in the data center today is rapidly accelerating and traditional data center systems have undergone considerable evolution in a very short period of time. As complexity continues to grow alongside the expectation that an organization's IT department should become ever-more "agile" and continue to deliver a quality end-user experience 24/7 (meaning no glitches, outages, application performance problems, etc.), it's important that IT professionals give monitoring the priority it deserves as a foundational IT discipline.

By understanding and addressing these top universal monitoring crimes, you can ensure your organization receives the benefit of sophisticated, tuned monitoring systems while also enabling a more proactive data center strategy now and in the future.

Hot Topics

The Latest

As discussions around AI "autonomous coworkers" accelerate, many industry projections assume that agents will soon operate alongside human staff in making decisions, taking actions, and managing tasks with minimal oversight. But a growing number of critics (including some of the developers building these systems) argue that the industry still has a long way to go to be able to treat AI agents like fully trusted teammates ...

Enterprise AI has entered a transformational phase where, according to Digitate's recently released survey, Agentic AI and the Future of Enterprise IT, companies are moving beyond traditional automation toward Agentic AI systems designed to reason, adapt, and collaborate alongside human teams ...

The numbers back this urgency up. A recent Zapier survey shows that 92% of enterprises now treat AI as a top priority. Leaders want it, and teams are clamoring for it. But if you look closer at the operations of these companies, you see a different picture. The rollout is slow. The results are often delayed. There's a disconnect between what leaders want and what their technical infrastructure can handle ...

Kyndryl's 2025 Readiness Report revealed that 61% of global business and technology leaders report increasing pressure from boards and regulators to prove AI's ROI. As the technology evolves and expectations continue to rise, leaders are compelled to generate and prove impact before scaling further. This will lead to a decisive turning point in 2026 ...

Cloudflare's disruption illustrates how quickly a single provider's issue cascades into widespread exposure. Many organizations don't fully realize how tightly their systems are coupled to thirdparty services, or how quickly availability and security concerns align when those services falter ... You can't avoid these dependencies, but you can understand them ...

If you work with AI, you know this story. A model performs during testing, looks great in early reviews, works perfectly in production and then slowly loses relevance after operating for a while. Everything on the surface looks perfect — pipelines are running, predictions or recommendations are error-free, data quality checks show green; yet outcomes don't meet the ground reality. This pattern often repeats across enterprise AI programs. Take for example, a mid-sized retail banking and wealth-management firm with heavy investments in AI-powered risk analytics, fraud detection and personalized credit-decisioning systems. The model worked well for a while, but transactions increased, so did false positives by 18% ...

Basic uptime is no longer the gold standard. By 2026, network monitoring must do more than report status, it must explain performance in a hybrid-first world. Networks are no longer just static support systems; they are agile, distributed architectures that sit at the very heart of the customer experience and the business outcomes ... The following five trends represent the new standard for network health, providing a blueprint for teams to move from reactive troubleshooting to a proactive, integrated future ...

APMdigest's Predictions Series concludes with 2026 AI Predictions — industry experts offer predictions on how AI and related technologies will evolve and impact business in 2026. Part 5, the final installment, covers AI's impacts on IT teams ...

APMdigest's Predictions Series concludes with 2026 AI Predictions — industry experts offer predictions on how AI and related technologies will evolve and impact business in 2026. Part 4 covers negative impacts of AI ...

APMdigest's Predictions Series concludes with 2026 AI Predictions — industry experts offer predictions on how AI and related technologies will evolve and impact business in 2026. Part 3 covers barriers and challenges for AI ...

Universal Monitoring Crimes and What to Do About Them - Part 2

Leon Adato

To help your organization increase data center efficiency and get the most benefit out of your monitoring solutions, here are the remaining universal monitoring crimes and what you can do about them:

Start with Universal Monitoring Crimes and What to Do About Them - Part 1

4. Flapping or sawtoothing alerts

When an alert repeatedly triggers (a device that keeps rebooting itself or processes keep deleting/creating temporary page files so that one moment it's over threshold, the next it's below, for example), that condition is known as flapping or sawtoothing.

What to do about it: These types of alerts have several possible resolutions based on what is supported by your monitoring solution and which best fits the specific situation:

■ GOOD: Suppress events within a window. Ignoring duplicated events within a certain period of time is often all you need to avoid meaningless duplicates.

■ ALSO GOOD: As mentioned previously, add a time delay to allow for self-resolution, avoid false-positives, and eliminate other potential issues that don't necessarily require a remediation response.

■ BETTER: Leverage "Reset" logic. Wait for a reset event before triggering a new alert of the same kind. Avoid making the reset logic merely the reverse of the trigger (if the alert is > 90%, the reset might be 90%). Instead, code the reset rules separately so that you might trigger when disk > 90% for 15 minutes, but it won't reset until it's 80% for 30.

■ BEST: Two-way communication with a ticket or alert management system. This is where the monitoring system communicates with the ticket and/or alert tracking system, so you can never cut the same alert for the same device until a human has actively corrected the original problem and closed the ticket.

5. No lab, test, or QA environments for your monitoring system

If your monitoring system is watching and alerting on mission-critical systems within the enterprise, then it is mission critical itself. But despite the fact that many organizations set up a proof-of-concept environment when evaluating monitoring solutions, once the production system is selected and rolled out, they fail to have any type of lab, test, or QA system that is maintained on an ongoing basis to help ensure the system is maintained.

What to do about it: Duh. Implement test, dev, and/or QA installations that serve to ensure your monitoring system has the oversight necessary for a mission-critical application.

■ TEST: An (often temporary) environment where patches and upgrades can be tested before attempting them in production.

■ DEV: An environment that mirrors production in terms of software, but where monitors for new equipment, applications, reports, or alerts can be set up and tested before rolling those solutions to production. And as mentioned earlier, this is the perfect place to also monitor your production monitoring environment.

■ QA: An environment that mirrors the previous version of production, so that if issues are found in production, they can be double-checked to confirm whether the problem was introduced in the last revision.

Note that I'm not implying you necessarily must have all three, but it's worth considering the value of at least one. Because "none" is a really bad choice.

Final thoughts

The rate of technical change in the data center today is rapidly accelerating and traditional data center systems have undergone considerable evolution in a very short period of time. As complexity continues to grow alongside the expectation that an organization's IT department should become ever-more "agile" and continue to deliver a quality end-user experience 24/7 (meaning no glitches, outages, application performance problems, etc.), it's important that IT professionals give monitoring the priority it deserves as a foundational IT discipline.

By understanding and addressing these top universal monitoring crimes, you can ensure your organization receives the benefit of sophisticated, tuned monitoring systems while also enabling a more proactive data center strategy now and in the future.

Hot Topics

The Latest

As discussions around AI "autonomous coworkers" accelerate, many industry projections assume that agents will soon operate alongside human staff in making decisions, taking actions, and managing tasks with minimal oversight. But a growing number of critics (including some of the developers building these systems) argue that the industry still has a long way to go to be able to treat AI agents like fully trusted teammates ...

Enterprise AI has entered a transformational phase where, according to Digitate's recently released survey, Agentic AI and the Future of Enterprise IT, companies are moving beyond traditional automation toward Agentic AI systems designed to reason, adapt, and collaborate alongside human teams ...

The numbers back this urgency up. A recent Zapier survey shows that 92% of enterprises now treat AI as a top priority. Leaders want it, and teams are clamoring for it. But if you look closer at the operations of these companies, you see a different picture. The rollout is slow. The results are often delayed. There's a disconnect between what leaders want and what their technical infrastructure can handle ...

Kyndryl's 2025 Readiness Report revealed that 61% of global business and technology leaders report increasing pressure from boards and regulators to prove AI's ROI. As the technology evolves and expectations continue to rise, leaders are compelled to generate and prove impact before scaling further. This will lead to a decisive turning point in 2026 ...

Cloudflare's disruption illustrates how quickly a single provider's issue cascades into widespread exposure. Many organizations don't fully realize how tightly their systems are coupled to thirdparty services, or how quickly availability and security concerns align when those services falter ... You can't avoid these dependencies, but you can understand them ...

If you work with AI, you know this story. A model performs during testing, looks great in early reviews, works perfectly in production and then slowly loses relevance after operating for a while. Everything on the surface looks perfect — pipelines are running, predictions or recommendations are error-free, data quality checks show green; yet outcomes don't meet the ground reality. This pattern often repeats across enterprise AI programs. Take for example, a mid-sized retail banking and wealth-management firm with heavy investments in AI-powered risk analytics, fraud detection and personalized credit-decisioning systems. The model worked well for a while, but transactions increased, so did false positives by 18% ...

Basic uptime is no longer the gold standard. By 2026, network monitoring must do more than report status, it must explain performance in a hybrid-first world. Networks are no longer just static support systems; they are agile, distributed architectures that sit at the very heart of the customer experience and the business outcomes ... The following five trends represent the new standard for network health, providing a blueprint for teams to move from reactive troubleshooting to a proactive, integrated future ...

APMdigest's Predictions Series concludes with 2026 AI Predictions — industry experts offer predictions on how AI and related technologies will evolve and impact business in 2026. Part 5, the final installment, covers AI's impacts on IT teams ...

APMdigest's Predictions Series concludes with 2026 AI Predictions — industry experts offer predictions on how AI and related technologies will evolve and impact business in 2026. Part 4 covers negative impacts of AI ...

APMdigest's Predictions Series concludes with 2026 AI Predictions — industry experts offer predictions on how AI and related technologies will evolve and impact business in 2026. Part 3 covers barriers and challenges for AI ...