/
Analytics (Advance functions)

Analytics (Advance functions)

Advance functions

Anomaly Detection

Anomaly detection is a powerful monitoring feature that uses algorithmic analysis to automatically identify unexpected behavior in metric data. Traditional threshold-based alerting often fails to account for trends, seasonality, or complex fluctuations in metrics. Anomaly detection algorithms overcome this limitation by analyzing historical patterns to establish dynamic boundaries, making it possible to detect deviations from normal behavior even as the data changes over time. Kloudfuse provides 4 Anomaly detection algorithms.

Key Arguments

Rolling Window Size:

  1. The rolling window size is used to calculate the standard deviation (std) for setting the band limits around expected values, which helps define the "normal" range.

    • A larger window size smooths the standard deviation calculation, reducing sensitivity to short-term fluctuations and providing a stable range for expected behavior. However, this may delay detection of rapid changes.

Bands :

  1. Band 1 (Narrow): Sets a tight range around expected values, making the algorithm highly sensitive to even small deviations. This band is ideal for detecting subtle anomalies, which could be early indicators of a potential issue.

  2. Band 2 (Moderate): Offers a balanced range, capturing moderate deviations without excessive sensitivity to minor fluctuations. This is suitable for general anomaly detection, where both significant and moderate changes are relevant.

  3. Band 3 (Wide): Provides the widest range, capturing only large deviations from the expected values. This is useful for minimizing false positives, focusing only on major anomalies that could indicate significant issues.

Seasonality :

Selects a single primary seasonal pattern based on the expected periodicity of the data. For instance, Daily seasonality is ideal for data that follows a consistent daily pattern, while Weekly is useful for data that repeats weekly.

 

Basic (Rolling-Quantile)

The Basic Anomaly Detection algorithm provides a straightforward way to identify unusual behavior in metric data by calculating rolling quantiles. It is well-suited for metrics without strong seasonal patterns or trends, where simple threshold-based monitoring may not be sufficient for capturing all anomalies. This algorithm allows you to define a range of expected values based on historical data, with deviations outside this range flagged as anomalies.

For example, Basic Anomaly Detection can help you spot unexpected drops in CPU utilization that might signal an issue with a server, or sudden spikes in network traffic that could indicate potential security incidents.

Fig-1 Basic Anomaly

 

Agile(SARIMA)

The Agile Anomaly Detection algorithm leverages the SARIMA (Seasonal AutoRegressive Integrated Moving Average) model to detect anomalies in metrics with predictable, short-term seasonal patterns and occasional abrupt level shifts. Agile is well-suited for metrics with daily or hourly cycles, allowing for rapid adaptation to sudden changes while accurately capturing short, repeating patterns.

Key Arguments:

Bands: Controls detection sensitivity (1,2,3)