Universal Monitoring Crimes and What to Do About Them - Part 2
May 23, 2018

Leon Adato
SolarWinds

Share this

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% for 15 minutes, but it won't reset until it's

■ 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.

Leon Adato is a Head Geek at SolarWinds
Share this

The Latest

October 17, 2019

As the data generated by organizations grows, APM tools are now required to do a lot more than basic monitoring of metrics. Modern data is often raw and unstructured and requires more advanced methods of analysis. The tools must help dig deep into this data for both forensic analysis and predictive analysis. To extract more accurate and cheaper insights, modern APM tools use Big Data techniques to store, access, and analyze the multi-dimensional data ...

October 16, 2019

Modern enterprises are generating data at an unprecedented rate but aren't taking advantage of all the data available to them in order to drive real-time, actionable insights. According to a recent study commissioned by Actian, more than half of enterprises today are unable to efficiently manage nor effectively use data to drive decision-making ...

October 15, 2019

According to a study by Forrester Research, an enhanced UX design can increase the conversion rate by 400%. If UX has become the ultimate arbiter in determining the success or failure of a product or service, let us first understand what UX is all about ...

October 10, 2019

The requirements of an APM tool are now much more complex than they've ever been. Not only do they need to trace a user transaction across numerous microservices on the same system, but they also need to happen pretty fast ...

October 09, 2019

Performance monitoring is an old problem. As technology has advanced, we've had to evolve how we monitor applications. Initially, performance monitoring largely involved sending ICMP messages to start troubleshooting a down or slow application. Applications have gotten much more complex, so this is no longer enough. Now we need to know not just whether an application is broken, but why it broke. So APM has had to evolve over the years for us to get there. But how did this evolution take place, and what happens next? Let's find out ...

October 08, 2019

There are some IT organizations that are using DevOps methodology but are wary of getting bogged down in ITSM procedures. But without at least some ITSM controls in place, organizations lose their focus on systematic customer engagement, making it harder for them to scale ...

October 07, 2019
OK, I admit it. "Service modeling" is an awkward term, especially when you're trying to frame three rather controversial acronyms in the same overall place: CMDB, CMS and DDM. Nevertheless, that's exactly what we did in EMA's most recent research: <span style="font-style: italic;">Service Modeling in the Age of Cloud and Containers</span>. The goal was to establish a more holistic context for looking at the synergies and differences across all these areas ...
October 03, 2019

If you have deployed a Java application in production, you've probably encountered a situation where the application suddenly starts to take up a large amount of CPU. When this happens, application response becomes sluggish and users begin to complain about slow response. Often the solution to this problem is to restart the application and, lo and behold, the problem goes away — only to reappear a few days later. A key question then is: how to troubleshoot high CPU usage of a Java application? ...

October 02, 2019

Operations are no longer tethered tightly to a main office, as the headquarters-centric model has been retired in favor of a more decentralized enterprise structure. Rather than focus the business around a single location, enterprises are now comprised of a web of remote offices and individuals, where network connectivity has broken down the geographic barriers that in the past limited the availability of talent and resources. Key to the success of the decentralized enterprise model is a new generation of collaboration and communication tools ...

October 01, 2019

To better understand the AI maturity of businesses, Dotscience conducted a survey of 500 industry professionals. Research findings indicate that although enterprises are dedicating significant time and resources towards their AI deployments, many data science and ML teams don't have the adequate tools needed to properly collaborate on, build and deploy AI models efficiently ...