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Alert Thresholds: Aggravating Mess or Indispensable Friends?

Setting up a network or application monitoring system involves creating alerts for critical parameters that need attention. Alerts are an integral part of monitoring and they should be easily understandable, provide actionable knowledge and should not make excessive noise. For an alert to be valuable to the user and meet those criteria, the right set of thresholds is essential. That really is the question then: How do you find the right threshold values for your alerts?

To determine and set the right threshold value, a deep level of understanding about the application, the server that hosts the application and the environment where the servers reside is required. Also needed is an application monitoring system that simplifies the process of isolating abnormal performance patterns in your environment. In the best case, you also have tools to assist with automatic threshold determination based on your real-world environment.

The Challenge of Dynamic Environments

When an application behaves as expected or if there is no significant variation in its day-to-day behavior, setting an alert threshold is a cakewalk. You know what is normal vs. what is unexpected. What if the application does not have a fixed baseline behavior? When it comes to applications with dynamic behavior patterns, even the Subject Matter Experts (SMEs) may find it challenging to set ideal thresholds, or have the patience to maintain and recalibrate them over time.

Let us take a look at some examples of where alerting can be difficult because finding the right threshold is challenging. Take Page Files for example. Their usage depends on workload, kernel and Operating System parameters and thus the usage differs from server to server.

Other examples are LDAP, Exchange, Lotus, etc., all of whose behavior depends on organization size, deployment platform and usage patterns. And then there is SQL - SQL server behavior changes based on number of applications connected to the DB.

The scenarios we saw now contribute to some major problems:

- False alerts flood your inbox leading you to a “Crying Wolf” situation. This occurs because you used a very low threshold and your mail box was flooded with alarms leaving you with no way to identify truly important alerts.

- You use a very high threshold value and there are almost no alerts. In such cases the first alert you may receive will be a critical level ticket raised by a user about application performance.

- The right threshold varies from server to server and also over time. This means you need to constantly monitor your servers and adapt to changing usage patterns. This could require you to invest time and resources on recalculating numerous threshold values, way more often than you'd like. That is easier said than done.

Since thresholds can change over time and from server to server, the investment of time and resources that goes into pulling up multiple reports, recalculating thresholds, and that too for multiple servers with every change can be huge. This is why it is imperative that you use a monitoring tool with the ability to automatically set your thresholds for alerts.

Your monitoring tools should be able to make use of the data it's already collecting for a monitored parameter and do the math to suggest the right threshold for a parameter. Such a tool can save time because you don't have to constantly revisit hundreds or thousands of metrics for every change in the network or services environment, pull reports and recalculate. Math should be reserved for more enjoyable leisure activities like calculating subnets. With automation you won’t have to find mean values and standard deviations to then determine what you think is the right threshold. And all this leads to reducing false alerts and giving you an opportunity to quickly cut thought the clutter and easily identify critical issues before users call the helpdesk.

Because application uptime is critical, automatic threshold capability will leave you with enough time to deal with issues that really need your attention. Alert tuning shouldn't be one more thing on your backlog list, they can be dependable partners who may bring you bad news, but in the best possible way. Who every thought you could enjoy alerts?

ABOUT Praveen Manohar

Praveen Manohar is a Head Geek at SolarWinds, a global IT management software provider based in Austin, Texas. He has 7 years of IT industry experience in roles such as Support Engineer, Product Trainer and Technical Consultant, and his expertise lies in technologies including NetFlow, Flexible NetFlow, Cisco NBAR, Cisco IPSLA, WMI and SNMP. Manohar gives strategic guidance for end users on applications, networks and performance monitoring tools.

Related Links:

www.solarwinds.com

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Alert Thresholds: Aggravating Mess or Indispensable Friends?

Setting up a network or application monitoring system involves creating alerts for critical parameters that need attention. Alerts are an integral part of monitoring and they should be easily understandable, provide actionable knowledge and should not make excessive noise. For an alert to be valuable to the user and meet those criteria, the right set of thresholds is essential. That really is the question then: How do you find the right threshold values for your alerts?

To determine and set the right threshold value, a deep level of understanding about the application, the server that hosts the application and the environment where the servers reside is required. Also needed is an application monitoring system that simplifies the process of isolating abnormal performance patterns in your environment. In the best case, you also have tools to assist with automatic threshold determination based on your real-world environment.

The Challenge of Dynamic Environments

When an application behaves as expected or if there is no significant variation in its day-to-day behavior, setting an alert threshold is a cakewalk. You know what is normal vs. what is unexpected. What if the application does not have a fixed baseline behavior? When it comes to applications with dynamic behavior patterns, even the Subject Matter Experts (SMEs) may find it challenging to set ideal thresholds, or have the patience to maintain and recalibrate them over time.

Let us take a look at some examples of where alerting can be difficult because finding the right threshold is challenging. Take Page Files for example. Their usage depends on workload, kernel and Operating System parameters and thus the usage differs from server to server.

Other examples are LDAP, Exchange, Lotus, etc., all of whose behavior depends on organization size, deployment platform and usage patterns. And then there is SQL - SQL server behavior changes based on number of applications connected to the DB.

The scenarios we saw now contribute to some major problems:

- False alerts flood your inbox leading you to a “Crying Wolf” situation. This occurs because you used a very low threshold and your mail box was flooded with alarms leaving you with no way to identify truly important alerts.

- You use a very high threshold value and there are almost no alerts. In such cases the first alert you may receive will be a critical level ticket raised by a user about application performance.

- The right threshold varies from server to server and also over time. This means you need to constantly monitor your servers and adapt to changing usage patterns. This could require you to invest time and resources on recalculating numerous threshold values, way more often than you'd like. That is easier said than done.

Since thresholds can change over time and from server to server, the investment of time and resources that goes into pulling up multiple reports, recalculating thresholds, and that too for multiple servers with every change can be huge. This is why it is imperative that you use a monitoring tool with the ability to automatically set your thresholds for alerts.

Your monitoring tools should be able to make use of the data it's already collecting for a monitored parameter and do the math to suggest the right threshold for a parameter. Such a tool can save time because you don't have to constantly revisit hundreds or thousands of metrics for every change in the network or services environment, pull reports and recalculate. Math should be reserved for more enjoyable leisure activities like calculating subnets. With automation you won’t have to find mean values and standard deviations to then determine what you think is the right threshold. And all this leads to reducing false alerts and giving you an opportunity to quickly cut thought the clutter and easily identify critical issues before users call the helpdesk.

Because application uptime is critical, automatic threshold capability will leave you with enough time to deal with issues that really need your attention. Alert tuning shouldn't be one more thing on your backlog list, they can be dependable partners who may bring you bad news, but in the best possible way. Who every thought you could enjoy alerts?

ABOUT Praveen Manohar

Praveen Manohar is a Head Geek at SolarWinds, a global IT management software provider based in Austin, Texas. He has 7 years of IT industry experience in roles such as Support Engineer, Product Trainer and Technical Consultant, and his expertise lies in technologies including NetFlow, Flexible NetFlow, Cisco NBAR, Cisco IPSLA, WMI and SNMP. Manohar gives strategic guidance for end users on applications, networks and performance monitoring tools.

Related Links:

www.solarwinds.com

Hot Topics

The Latest

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...

When most people think about cybersecurity, they picture firewalls, encryption, and access controls — technical tools designed to protect systems and data. But beneath the technology lies a deeper set of principles about trust, decision-making, and resilience ... The best leaders don't eliminate risk. They manage it intelligently. And in many ways, cybersecurity offers a surprisingly useful playbook for doing exactly that ...