It’s time, take your things and let’s move on to more modern monitoring. Relax, I know how difficult the changes are for you, but if you were able to accept the arrival of DTT and the euro, you sure got this!

But first let us do a little review:

Traditional system monitoring solutions rely on polling different meters, such as the Simple Network Management Protocol (SNMP), to retrieve data and react to it.

When an issue requiring attention is detected, an event is triggered, which can be notified through email to the administrator or by launching an alert.

Consequently, the administrator responds according to the nature of the problem.

However, this centralized approach to monitoring requires a considerable amount of resources.

You knew it?

Due to the “pull” nature of the requests, gaps are generated in the data and these could lack sufficient granularity.

In response to this limitation, the adoption of a telemetry-based monitoring solution has emerged as a promising alternative.

The day has come: discover more modern monitoring

By making the switch to a modern approach to monitoring, you gain access to smarter and richer solutions in terms of anomaly detection.

This transition represents a great advantage in the field of system monitoring.

In addition, another compelling reason to implement modern monitoring is the growing number of systems that rely on accurate monitoring to enable automated infrastructure operations, such as scaling nodes up or down.

If monitoring fails or is not accurate enough, disruptions can arise in the expected scalability of an application.

Therefore, reliable and accurate monitoring is essential to ensure the proper functioning of the systems.

To improve the efficiency and accuracy of surveillance systems, organizations are exploring smarter, more advanced solutions.

Telemetry vs. Poll: Differences and benefits in modern system monitoring

When it comes to implementing telemetry in surveillance systems, it is important to understand the differences between the streaming and polling approaches.

While streaming telemetry can be more complex, its design offers greater scalability, evidenced by public cloud providers such as Amazon, Microsoft, and Google.

These tech giants manage millions of hosts and endpoints that require constant monitoring.

As a result, they have developed telemetry and monitoring channels with no single points of failure, enabling them to gain the level of intelligence and automation needed to operate at a great scale in their data centers.

Learning from these experiences can be invaluable when building your own monitoring solutions.

In contrast, survey-based monitoring solutions may face scalability challenges.

Increasing the polling interval for a specific performance counter increases the load on the system being monitored.

Some meters are lightweight and can be probed frequently, but other heavier meters generate significant overhead.

Consistent data transmission may appear, at first glance, to involve greater overhead compared to a polling solution.

However, thanks to technological advances, lightweight solutions have been developed.

In many cases, data flows through a flow query engine that enables outlier detection while storing all data to support trend analysis and machine learning.

This architecture is known as lambda architecture and is widely used in applications that go beyond monitoring, such as in the Internet of Things (IoT) devices and sensors.

It provides real-time alerts for values outside normal limits, while enabling cost-effective storage of recorded data, providing deeper analysis in a low-cost data warehouse.

The ability to have a large amount of recorded data allows for comprehensive analyses of transmitted values.

System monitoring: Smart approaches and machine learning for accurate alerts

In the system monitoring area, it is crucial to ensure data quality to obtain accurate and relevant alerts.

Most monitoring tools offer the ability to customize alert ranges.

*For example, you may want to receive an alert when CPU usage exceeds 80% on certain systems, while on other systems a high level of CPU usage may be part of its normal operation.

However, finding the right balance can be tricky:

  • On the one hand, you don’t want alerts to overwhelm administrators with irrelevant information.
  • On the other hand, you also don’t want to set too lax thresholds that hide critical issues in your data center.

To address this dichotomy, it is advisable to use intelligent or dynamic monitoring approaches.

These approaches capture a baseline for each system and only trigger alerts when values are outside normal limits for both the specific server and the corresponding time frame.

As more data is collected, many monitoring tools are deploying machine learning systems to perform deeper analysis of the data.

This advanced processing makes it possible to generate smarter alerts based on the specific workload of each system.

Machine learning is used to detect subtle patterns and anomalies that might go unnoticed by traditional alert rules.

However, it is important to note that it is essential to verify that these smart alerts work correctly and provide the expected alerts.

Extensive testing and result validation is necessary to ensure that alerts are generated accurately and in a timely manner.

*That way, a more efficient and reliable surveillance system will be achieved.

Monitoring continuity: Key strategies and approaches for detecting problems in systems

As your organization seeks to implement smarter monitoring, the crucial question arises:

How do we detect problems or outages in our monitoring systems?

As automation connected to these systems becomes increasingly reliant, monitoring becomes an even greater challenge.

There are several measures that can be taken to ensure monitoring continuity:

  • First, it is critical to create redundancy in the infrastructure, either by deploying virtual machines or leveraging platform-as-a-service (PaaS) services across different data centers or cloud regions. This ensures that, in the event of failures at a point, alternative systems are in place to support monitoring.
  • Another option is to establish a custom or secondary alert mechanism that verifies the status of the primary monitoring system and acts as an additional security layer.
  • It is also possible to implement an alert process, which generates alerts at regular intervals, and have a scaled mechanism that issues an additional alert if the process is not activated as expected.

In addition to these approaches, it is important to ensure that the alert mechanism covers all application layers and it is not limited to just one.

*For example, it is necessary to perform tests and monitoring in the web layer, the cache storage layer and the database, to detect any failure or anomaly in any of them and receive relevant alerts.

Keeping online supervision requires a proactive and solid approach in terms of architecture and strategy.

By implementing these security and verification measures, it is ensured that any problems or interruptions in monitoring systems are quickly detected and addressed, allowing a timely response to maintain the proper functioning of critical systems.


Taking the leap to more modern monitoring is a smart and necessary decision to ensure proper system functioning.

Although the changes may seem intimidating, remember that you survived the Ibook and egg-free mayonnaise, so this should be a piece of cake!

Telemetry is presented as a promising alternative, offering smarter solutions and more accurate anomaly detection.

In addition, the implementation of redundancy measures, custom alert mechanisms and extensive testing at all layers of the application will ensure reliable and timely monitoring.

So get ready to embrace modern monitoring and leave outdated methods behind!

Remember, in the world of monitoring, to be modern is to be more modern than the average human.

And you’re ready to be the “cool kid” of monitoring!

Do you know Pandora FMS monitoring system?

La solución de monitorización total para una completa observabilidad

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