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Centreon 21.04 Released

Centreon released Centreon 21.04.

Centreon makes it more efficient than ever to manage dynamic IT infrastructure, covering cloud technologies, traditional equipment, edge computing, and the networks that interconnect them all. The 21.04 release further augments ITOps teams efficiency with connectors that include more automation with the optimized Auto-Discovery engine, artificial intelligence and major improvements in alert management.

The Auto-Discovery engine is a key asset to ensure that 100% of the IT landscape is being constantly monitored and provide IT teams with tight control. The Auto-Discovery engine of Centreon 21.04 has been further improved to facilitate the work of operations teams when it comes to managing the system’s complexity: automatic assignment of host groups, categories, and severity.

This Auto-Discovery engine is based on a ready-to-use connector library (over 500-and counting), which contains both ready-to-use configuration templates and 287 discovery rules applicable to all types of equipment.

Centreon Auto-Discovery engine will automatically scan, detect, configure, categorize, and monitor-infrastructure domains like Amazon AWS services such as EBS, EC2, EFS, RDS; Microsoft Azure services such as Azure Automation, Elastic Pool, Event Grid, Firewall, Key Vault, Load Balancer, Public IP, ServiceBus, SignalR; Google Cloud Compute services such as CloudSQL, Engine, Storage; Kubernetes clusters, VmWare clusters, Nutanix servers; SD-WAN such as Versa Director, Cisco Meraki, VmWare VeloCloud; WiFi Networks such as Aruba.

Anomaly Detection relies on artificial intelligence and machine learning to alert on unusual, dysfunctional behavior, bypassing the habitual thresholds. Based on the Centreon Cloud to implement the resource-intensive machine learning algorithms and thus preserve the supervision servers, this new architecture combines Centreon’s unrivalled monitoring capacity with the cloud’s limitless computing power. Centreon monitoring servers naturally integrate with the Centreon Cloud Platform to add power when needed if needed.

When it comes to knowing which metric Anomaly Detection should be applied to, Centreon also provides the answer. Centreon 21.04 comes with an Auto-Suggestion feature which role is to automatically suggest a list of relevant metrics on which to apply Anomaly Detection. All Centreon Business Edition customers can now access this feature.

The Latest

I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

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 ...

Centreon 21.04 Released

Centreon released Centreon 21.04.

Centreon makes it more efficient than ever to manage dynamic IT infrastructure, covering cloud technologies, traditional equipment, edge computing, and the networks that interconnect them all. The 21.04 release further augments ITOps teams efficiency with connectors that include more automation with the optimized Auto-Discovery engine, artificial intelligence and major improvements in alert management.

The Auto-Discovery engine is a key asset to ensure that 100% of the IT landscape is being constantly monitored and provide IT teams with tight control. The Auto-Discovery engine of Centreon 21.04 has been further improved to facilitate the work of operations teams when it comes to managing the system’s complexity: automatic assignment of host groups, categories, and severity.

This Auto-Discovery engine is based on a ready-to-use connector library (over 500-and counting), which contains both ready-to-use configuration templates and 287 discovery rules applicable to all types of equipment.

Centreon Auto-Discovery engine will automatically scan, detect, configure, categorize, and monitor-infrastructure domains like Amazon AWS services such as EBS, EC2, EFS, RDS; Microsoft Azure services such as Azure Automation, Elastic Pool, Event Grid, Firewall, Key Vault, Load Balancer, Public IP, ServiceBus, SignalR; Google Cloud Compute services such as CloudSQL, Engine, Storage; Kubernetes clusters, VmWare clusters, Nutanix servers; SD-WAN such as Versa Director, Cisco Meraki, VmWare VeloCloud; WiFi Networks such as Aruba.

Anomaly Detection relies on artificial intelligence and machine learning to alert on unusual, dysfunctional behavior, bypassing the habitual thresholds. Based on the Centreon Cloud to implement the resource-intensive machine learning algorithms and thus preserve the supervision servers, this new architecture combines Centreon’s unrivalled monitoring capacity with the cloud’s limitless computing power. Centreon monitoring servers naturally integrate with the Centreon Cloud Platform to add power when needed if needed.

When it comes to knowing which metric Anomaly Detection should be applied to, Centreon also provides the answer. Centreon 21.04 comes with an Auto-Suggestion feature which role is to automatically suggest a list of relevant metrics on which to apply Anomaly Detection. All Centreon Business Edition customers can now access this feature.

The Latest

I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

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 ...