Detect Application/infrastructure anomalies

Detect Application/ infrastructure anomalies

The detection of anomalous activities in the infrastructure or the components of the application stack is very critical. Through anomaly detection, we can focus on the identification of data points and situations that are not complementary to the desired pattern.

Automated anomaly detection is all about identifying unusual patterns in the series of data based on increasing scale and margin. Advances in Machine Learning, Artificial Intelligence, and Deep Learning offer powerful log analytic solutions that enable auto-detection of anomalies. Adoption of anomaly detection makes one’s way of life at work efficient, faster, and more productive.


What are anomalies

Anomalies are broadly classified into three types:

  • Point Anomaly: A dataset is said to be Point Anomaly if it is too far off from the rest of the data.
  • Contextual Anomaly: When a group of data points deviates significantly from the rest of the data points within the same context, it is considered as a Contextual Anomaly.
  • Collective Anomaly: Combination of many instances together is considered as a Collective Anomaly.

Why it is required

  • Effortless: Big data available can be easily fed into the automated anomaly detection system to identify the source of significant anomalies.
  • Faster: Automated anomaly detection has led to real-time analytics to reality. This allows effective responsiveness. 
  • Better: It has an ability to enable the decision to manage an exception and also automate the smooth run of the application.
  • Cost effective: Advancement in technologies have significantly led to operation cost optimization.

How it helps

Anomaly detection keeps an eye on streaming data and compares it with the baseline. Anomalies are generated if any pattern breaches the baseline in the production environment. Such breaches may be notified in real-time with detailed insights into the root cause of an anomaly.

Predictive Analytics in anomaly detection is highly productive in terms of identification of faults and strange pattern in large and complex datasets. It’s been found crucial for the systems that require smooth and secure operations.

Proactive Anomaly Detection and Alerting using Cavisson

Cavisson NetDiagnostics monitors each layer of the application stack and collects the data in real-time. It applies powerful computing algorithms to slice and dice the streaming data along with the visual representation to do root cause analysis.

This is combined with advanced machine-learning algorithms to identify a pattern and do proactive anomaly detection.

Cavisson pioneered following ML-driven alerts to reduce false alarms upon identification of anomalies. 

  • Dynamic Baseline
  • Load-Index based Alerts
  • Predictive Alerts
  • Correlated Alerts
  • Hotspot

To know more please follow Detect enterprise infrastructure/application anomalies– https://www.cavisson.com/detect-enterprise-infrastructure-application-anomalies/ offering from Cavisson.

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