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

Leon Adato
Learn more about SolarWinds

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
Learn more about SolarWinds

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.

Related Links

Hot Topics

Related Vendors

The Latest

When the Database Is the Problem, Is APM Enough?

Production incidents rarely announce themselves as database problems. They appear as slow transactions, timeouts, rising response times, or an application struggling under a workload it previously handled. APM provides an essential starting point. It can identify a slow transaction path, highlight an affected service, and show that a database dependency is consuming more time than expected. But identifying the database as part of the problem is not the same as explaining what is happening inside it ...

From Cleanup to Prevention: The Future of Cloud-Native Cost Optimization

Cloud teams are under constant pressure to reduce spend without slowing development or increasing operational risk. They are deploying autoscalers, rightsizing workloads, enforcing resource requests, reviewing utilization dashboards, and building FinOps processes around cloud-native environments. Yet the results often disappoint ...

The Real Enterprise AI Risk Isn't Hallucinations

Ask most IT leaders about their biggest concern with AI and you'll hear the same answer: hallucinations ... Today, however, the conversation has shifted ... As organizations move beyond chatbots and experiments, they are increasingly deploying AI agents that perform multi-step tasks. These systems retrieve documents, query databases, call APIs, generate reports, write code, and make recommendations. The issue is not whether the model can reason. The issue is whether the organization can see, verify, and govern the decisions being made along the way ...

Beyond Buy vs. Build: The New Playbook for Data Pipelines

While organizations want to take control of their telemetry, building telemetry pipelines from scratch can be a very daunting, complicated task, even when leveraging open-source standards like OpenTelemetry. It requires specialized knowledge across distributed systems, data engineering, and security. This fragmented approach across systems causes higher operational costs; it puts a strain on resources and reduces efficiency as teams have to work with different interfaces and processes ...

The Corporate Network Is Fading - Here's What Replaces It

For decades, enterprise networks were designed around a simple assumption: work happened inside the office. Applications lived in centralized data centers, employees connected through internal infrastructure, and security focused on protecting the perimeter that surrounded everything ... But the way organizations operate today bears little resemblance to that environment. Cloud platforms host critical applications, employees connect from homes and airports as often as they do from offices, and partners collaborate through shared systems that exist far beyond corporate walls. In short, the corporate network no longer resembles the environment it was designed to protect ...

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As an analyst who researches how IT organizations design, build, and operate their networks, I find that network data is a constant source of pain. Network teams struggle with data quality, fragmentation, authority, access, and trust. And these issues undermine everything they try to do. Here are the numbers: Only 45% of network teams are completely confident in the accuracy of their network source of truth, which documents the intent of their network ...

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The 2026 Global Data Center Survey from Uptime Institute reveals an industry navigating workforce constraints, escalating outage expenses, even as rising costs remain the top concern for management teams ...

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The next observability gap may not be in the code. It may be under the rack. That sounds strange until you think about how AI incidents actually feel in the middle of an investigation ... The application dashboard may be accurate. It may also be stopping at the wrong boundary. AI systems depend on software, but they also depend on a dense physical stack: racks, power paths, thermal margin, maintenance activity and, in many environments, liquid cooling. Those physical dependencies can change slowly before they look like a software incident ...

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Certificate expiration is the rare outage you can see coming. Every TLS certificate carries the date it stops working, so the moment it will begin breaking connections is knowable in advance. That's what makes an expired certificate such a frustrating way to lose a service. What's changing now is how often that date comes around ...

Enterprise Cloud Observability: Choosing the Right Starting Point

Enterprises operate different combinations of workloads across cloud, hybrid and multicloud environments. For business-critical workloads, teams need to consider monitoring and observability early so they can detect health issues, investigate failures, and understand operational impact. Organizations place workloads on cloud platforms based on a combination of technical requirements, economics, existing dependencies, organizational standards, and business priorities. Their monitoring priorities therefore depend on what they operate and where those systems run. Those priorities will not look the same for every organization ...