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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

As businesses increasingly rely on high-performance applications to deliver seamless user experiences, the demand for fast, reliable, and scalable data storage systems has never been greater. Redis — an open-source, in-memory data structure store — has emerged as a popular choice for use cases ranging from caching to real-time analytics. But with great performance comes the need for vigilant monitoring ...

Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...

AI is the catalyst for significant investment in data teams as enterprises require higher-quality data to power their AI applications, according to the State of Analytics Engineering Report from dbt Labs ...

Misaligned architecture can lead to business consequences, with 93% of respondents reporting negative outcomes such as service disruptions, high operational costs and security challenges ...

A Gartner analyst recently suggested that GenAI tools could create 25% time savings for network operational teams. Where might these time savings come from? How are GenAI tools helping NetOps teams today, and what other tasks might they take on in the future as models continue improving? In general, these savings come from automating or streamlining manual NetOps tasks ...

IT and line-of-business teams are increasingly aligned in their efforts to close the data gap and drive greater collaboration to alleviate IT bottlenecks and offload growing demands on IT teams, according to The 2025 Automation Benchmark Report: Insights from IT Leaders on Enterprise Automation & the Future of AI-Driven Businesses from Jitterbit ...

A large majority (86%) of data management and AI decision makers cite protecting data privacy as a top concern, with 76% of respondents citing ROI on data privacy and AI initiatives across their organization, according to a new Harris Poll from Collibra ...

According to Gartner, Inc. the following six trends will shape the future of cloud over the next four years, ultimately resulting in new ways of working that are digital in nature and transformative in impact ...

2020 was the equivalent of a wedding with a top-shelf open bar. As businesses scrambled to adjust to remote work, digital transformation accelerated at breakneck speed. New software categories emerged overnight. Tech stacks ballooned with all sorts of SaaS apps solving ALL the problems — often with little oversight or long-term integration planning, and yes frequently a lot of duplicated functionality ... But now the music's faded. The lights are on. Everyone from the CIO to the CFO is checking the bill. Welcome to the Great SaaS Hangover ...

Regardless of OpenShift being a scalable and flexible software, it can be a pain to monitor since complete visibility into the underlying operations is not guaranteed ... To effectively monitor an OpenShift environment, IT administrators should focus on these five key elements and their associated metrics ...