The pace at which technology is evolving these days, it’s challenging to monitor the modern infrastructure. The volume, variety and velocity of data, the change in business dynamism and associated scalability, growth, M&A makes things complex.
Anomaly detection reduces the complexities associated with monitoring and managing performance of the infrastructure and applications that drive revenue and profitability. With anomaly detection we can focus on identification of data points, situations that are not ideal to the desired pattern.
Using Anomaly detection momentary problems can be detected along with actionable insights into specific incidents within the infrastructure (server, application, database, other components).
The value of anomaly detection significantly increases when end-to-end monitoring is enabled (client-side, infrastructure side, as well as logs). Anomaly detection also provides actionable insights into business anomalies, such as, Order, revenue, etc. Machine learning, Artificial Intelligence, and analytics are integral components of anomaly detection.
Types of anomalies
Anomalies are broadly classified into three types
A dataset is said to be Point anomaly if it is too far off from the rest of the data.
When a group of data points significantly deviates from the rest of the data points but in same context, it is considered as an anomaly.
Combination of many instances together considered as an anomaly.
What It does and why
Anomaly detector keep an eye on streaming data and compares 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, strange pattern in large and complex datasets. It’s been found crucial for the systems which require smooth and secure operations.
How Cavisson Alerting mechanism helps in identifying anomalies
Cavisson software monitors each layer of application stack and collects the real-time streaming data. All monitoring data is stored in a proprietary big data engine, that allows complex computing algorithm for detailed analysis in real-time and combines it with advanced machine learning to detect anomalies at the earliest.
Cavisson pioneered ML-driven Load index-based alerts to reduce false alarms.