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

Leon Adato

Monitoring is a critical aspect of any data center operation, yet it often remains the black sheep of an organization's IT strategy: an afterthought rather than a core competency. Because of this, many enterprises have a monitoring solution that appears to have been built by a flock of "IT seagulls" — technicians who swoop in, drop a smelly and offensive payload, and swoop out. Over time, the result is layer upon layer of offensive payloads that are all in the same general place (your monitoring solution) but have no coherent strategy or integration.

Believe it or not, this is a salvageable scenario. By applying a few basic techniques and monitoring discipline, you can turn a disorganized pile of noise into a monitoring solution that provides actionable insight. For the purposes of this piece, let's assume you've at least implemented some type of monitoring solution within your environment.

At its core, the principle of monitoring as a foundational IT discipline is designed to help IT professionals escape the short-term, reactive nature of administration, often caused by insufficient monitoring, and become more proactive and strategic. All too often, however, organizations are instead bogged down by monitoring systems that are improperly tuned — or not tuned at all — for their environment and business needs. This results in unnecessary or incorrect alerts that introduce more chaos and noise than order and insight, and as a result, cause your staff to value monitoring even less.

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

1. Fixed thresholds

Monitoring systems that trigger any type of alert at a fixed value for a group of devices are the "weak tea" of solutions. While general thresholds can be established, it is statistically impossible that every single device is going to adhere to the same one, and extremely improbable that even a majority will.

Even a single server has utilization that varies from day to day. A server that usually runs at 50 percent CPU, for example, but spikes to 95 percent at the end of the month is perfectly normal — but fixed thresholds can cause this spike to trigger. The result is that many organizations create multiple versions of the same alert (CPU Alert for Windows IIS-DMZ; CPU Alert for Windows IIS-core; CPU Alert for Windows Exchange CAS, and so on). And even then, fixed thresholds usually throw more false positives than anyone wants.

What to do about it:

■ GOOD: Enable per-device (and per-service) thresholds. Whether you do this within the tool or via customizations, you should ultimately be able to have a specific threshold for each device so that machines that have a specific threshold trigger at the correct time, and those that do not get the default.

■ BETTER: Use existing monitoring data to establish baselines for "normal" and then trigger when usage deviates from that baseline. Note that you may need to consider how to address edge cases that may require a second condition to help define when a threshold is triggered.

2. Lack of monitoring system oversight

While it's certainly important to have a tool or set of tools that monitor and alert on mission-critical systems, it's also important to have some sort of system in place to identify problems within the monitoring solution itself.

What to do about it: Set up a separate instance of a monitoring solution that keeps track of the primary, or production, monitoring system. It can be another copy of the same tool or tools you are using in production, or a separate solution, such as open source, vendor-provided, etc.

For another option to address this, see the discussion on lab and test environments in Part 2 of this blog.

3. Instant alerts

There are endless reasons why instant alerts — when your monitoring system triggers alerts as soon as a condition is detected — can cause chaos in your data center. For one thing, monitoring systems are not infallible and may detect "false positive" alerts that don't truly require a remediation response. For another, it's not uncommon for problems to appear for a moment and then disappear. Still some other problems aren't actionable until they've persisted for a certain amount of time. You get the idea.

What to do about it: Build a time delay into your monitoring system's trigger logic where a CPU alert, for example, would need to have all of the specified conditions persist for something like 10 minutes before any action would be needed. Spikes lasting longer than 10 minutes would require more direct intervention while anything less represents a temporary spike in activity that doesn't necessarily indicate a true problem.

Read Universal Monitoring Crimes and What to Do About Them - Part 2, for more monitoring tips.

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

Leon Adato

Monitoring is a critical aspect of any data center operation, yet it often remains the black sheep of an organization's IT strategy: an afterthought rather than a core competency. Because of this, many enterprises have a monitoring solution that appears to have been built by a flock of "IT seagulls" — technicians who swoop in, drop a smelly and offensive payload, and swoop out. Over time, the result is layer upon layer of offensive payloads that are all in the same general place (your monitoring solution) but have no coherent strategy or integration.

Believe it or not, this is a salvageable scenario. By applying a few basic techniques and monitoring discipline, you can turn a disorganized pile of noise into a monitoring solution that provides actionable insight. For the purposes of this piece, let's assume you've at least implemented some type of monitoring solution within your environment.

At its core, the principle of monitoring as a foundational IT discipline is designed to help IT professionals escape the short-term, reactive nature of administration, often caused by insufficient monitoring, and become more proactive and strategic. All too often, however, organizations are instead bogged down by monitoring systems that are improperly tuned — or not tuned at all — for their environment and business needs. This results in unnecessary or incorrect alerts that introduce more chaos and noise than order and insight, and as a result, cause your staff to value monitoring even less.

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

1. Fixed thresholds

Monitoring systems that trigger any type of alert at a fixed value for a group of devices are the "weak tea" of solutions. While general thresholds can be established, it is statistically impossible that every single device is going to adhere to the same one, and extremely improbable that even a majority will.

Even a single server has utilization that varies from day to day. A server that usually runs at 50 percent CPU, for example, but spikes to 95 percent at the end of the month is perfectly normal — but fixed thresholds can cause this spike to trigger. The result is that many organizations create multiple versions of the same alert (CPU Alert for Windows IIS-DMZ; CPU Alert for Windows IIS-core; CPU Alert for Windows Exchange CAS, and so on). And even then, fixed thresholds usually throw more false positives than anyone wants.

What to do about it:

■ GOOD: Enable per-device (and per-service) thresholds. Whether you do this within the tool or via customizations, you should ultimately be able to have a specific threshold for each device so that machines that have a specific threshold trigger at the correct time, and those that do not get the default.

■ BETTER: Use existing monitoring data to establish baselines for "normal" and then trigger when usage deviates from that baseline. Note that you may need to consider how to address edge cases that may require a second condition to help define when a threshold is triggered.

2. Lack of monitoring system oversight

While it's certainly important to have a tool or set of tools that monitor and alert on mission-critical systems, it's also important to have some sort of system in place to identify problems within the monitoring solution itself.

What to do about it: Set up a separate instance of a monitoring solution that keeps track of the primary, or production, monitoring system. It can be another copy of the same tool or tools you are using in production, or a separate solution, such as open source, vendor-provided, etc.

For another option to address this, see the discussion on lab and test environments in Part 2 of this blog.

3. Instant alerts

There are endless reasons why instant alerts — when your monitoring system triggers alerts as soon as a condition is detected — can cause chaos in your data center. For one thing, monitoring systems are not infallible and may detect "false positive" alerts that don't truly require a remediation response. For another, it's not uncommon for problems to appear for a moment and then disappear. Still some other problems aren't actionable until they've persisted for a certain amount of time. You get the idea.

What to do about it: Build a time delay into your monitoring system's trigger logic where a CPU alert, for example, would need to have all of the specified conditions persist for something like 10 minutes before any action would be needed. Spikes lasting longer than 10 minutes would require more direct intervention while anything less represents a temporary spike in activity that doesn't necessarily indicate a true problem.

Read Universal Monitoring Crimes and What to Do About Them - Part 2, for more monitoring tips.

Hot Topics

The Latest

According to Auvik's 2025 IT Trends Report, 60% of IT professionals feel at least moderately burned out on the job, with 43% stating that their workload is contributing to work stress. At the same time, many IT professionals are naming AI and machine learning as key areas they'd most like to upskill ...

Businesses that face downtime or outages risk financial and reputational damage, as well as reducing partner, shareholder, and customer trust. One of the major challenges that enterprises face is implementing a robust business continuity plan. What's the solution? The answer may lie in disaster recovery tactics such as truly immutable storage and regular disaster recovery testing ...

IT spending is expected to jump nearly 10% in 2025, and organizations are now facing pressure to manage costs without slowing down critical functions like observability. To meet the challenge, leaders are turning to smarter, more cost effective business strategies. Enter stage right: OpenTelemetry, the missing piece of the puzzle that is no longer just an option but rather a strategic advantage ...

Amidst the threat of cyberhacks and data breaches, companies install several security measures to keep their business safely afloat. These measures aim to protect businesses, employees, and crucial data. Yet, employees perceive them as burdensome. Frustrated with complex logins, slow access, and constant security checks, workers decide to completely bypass all security set-ups ...

Image
Cloudbrink's Personal SASE services provide last-mile acceleration and reduction in latency

In MEAN TIME TO INSIGHT Episode 13, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses hybrid multi-cloud networking strategy ... 

In high-traffic environments, the sheer volume and unpredictable nature of network incidents can quickly overwhelm even the most skilled teams, hindering their ability to react swiftly and effectively, potentially impacting service availability and overall business performance. This is where closed-loop remediation comes into the picture: an IT management concept designed to address the escalating complexity of modern networks ...

In 2025, enterprise workflows are undergoing a seismic shift. Propelled by breakthroughs in generative AI (GenAI), large language models (LLMs), and natural language processing (NLP), a new paradigm is emerging — agentic AI. This technology is not just automating tasks; it's reimagining how organizations make decisions, engage customers, and operate at scale ...

In the early days of the cloud revolution, business leaders perceived cloud services as a means of sidelining IT organizations. IT was too slow, too expensive, or incapable of supporting new technologies. With a team of developers, line of business managers could deploy new applications and services in the cloud. IT has been fighting to retake control ever since. Today, IT is back in the driver's seat, according to new research by Enterprise Management Associates (EMA) ...

In today's fast-paced and increasingly complex network environments, Network Operations Centers (NOCs) are the backbone of ensuring continuous uptime, smooth service delivery, and rapid issue resolution. However, the challenges faced by NOC teams are only growing. In a recent study, 78% state network complexity has grown significantly over the last few years while 84% regularly learn about network issues from users. It is imperative we adopt a new approach to managing today's network experiences ...

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Broadcom

From growing reliance on FinOps teams to the increasing attention on artificial intelligence (AI), and software licensing, the Flexera 2025 State of the Cloud Report digs into how organizations are improving cloud spend efficiency, while tackling the complexities of emerging technologies ...