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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.

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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

AI observability serves several roles across an organization. AI engineers use it to build, evaluate, and improve AI systems. SREs use it to operate those systems reliably and efficiently in production, including managing their cost. Leaders need it to connect AI behavior and operational performance to business outcomes. The common need is a clear understanding of whether AI systems are delivering the intended result ...

Part 4 outlines the challenges enterprise face when adopting AI observability. The main challenge is that AI complexity compounds at every stage of maturity. LLM-based applications introduce new behavior, dependencies, and quality considerations, while Agentic AI adds further failure modes as systems make decisions and take actions ...

Part 3 reports on the current state of AI observability. Most experts agree: enterprises are moving faster with AI than they are with the operational discipline needed to run it safely ...

When discussing AI Observability, one of the first questions that arises: Is this just an extension of traditional observability or is this a whole new type of tool? Part 2 compares traditional observability with AI observability ...

In this extensive 14-part series that will post over the next three weeks, experts across observability and AI will provide insight and opinions on the need for AI Observability, the challenges, the benefits, the current state, and predictions for the future ...

Amid rising complexity, organizations are moving quickly toward agent-driven operations: AgenticOps, according to The Impact of Agentic AI on Network Operations, a new report from Cisco ...

AI, automation, and enterprise software give organizations an extraordinary amount of leverage. Work that once required hours can happen in minutes. Information buried across systems can be analyzed almost instantly. Complex workflows can be coordinated across teams with far less manual intervention. That leverage creates a temptation to start with the technology ...

Everyone wants to talk about models. Compute clusters. Benchmark scores that climb a fraction of a point and make headlines anyway. It's an easy conversation to have because it's measurable. But it's also increasingly beside the point ...

For more than a decade, the default infrastructure play has been simple. Move more workloads to the cloud, retire on-premises gear and treat the data center as a cost to shrink. That logic held while public cloud offered near-limitless capacity at predictable prices, and AI demand is now testing both halves of that assumption ...

Most enterprise IT leaders can tell you exactly how many applications are running across their organization. They can surface a dashboard showing telecom spend by region, pull a report on SaaS license utilization, or call up a real-time view of mobile device inventory. The data is there, and IT leaders have largely delivered on the promise of visibility. Yet, in most organizations, that visibility has not translated into business value ...