Cloud Resource Anomaly Detection with Behavioral Embeddings
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Cloud systems often exhibit jittery behavior, obfuscating temporal anomaly detection and preventing the identification of unstable resources due to misconfiguration, which can be misinterpreted as normal operation.
Innovation Solution
A resource behavior model, such as an autoencoder, is trained on historical health time-series data to encode and decode resource data, allowing for the determination of reconstruction loss and similarity comparison with other resources to identify anomalous behavior.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If temporal anomaly detection is used on resource alert logs, then root cause analysis capability is improved, but jittery or loud behavior of cloud systems obfuscates the detection and prevents identification of unstable resources
Solution Approach 1:
The patent extracts and removes the jittery or loud behavior components from the resource time-series data before performing anomaly detection. By separating these harmful variations from the underlying signal, the detection system can identify true anomalies without being obfuscated by normal system jitter, directly resolving the contradiction between detection accuracy and noise interference.
Solution Approach 2:
The patent introduces an intermediary processing layer that transforms raw resource time-series data into a denoised representation suitable for anomaly detection. This intermediary step acts as a mediator between the noisy original data and the detection algorithm, enabling accurate anomaly identification while filtering out jittery behavior through techniques like autoencoders or statistical smoothing.
2Reliability
If cloud systems are configured with monitoring metrics, then service health monitoring capability is improved, but misconfigured resources generate false positives that are misinterpreted as normal operation
Solution Approach 1:
The patent segments the analysis process into distinct stages: data collection, denoising/normalization, anomaly detection, and validation. By dividing the monitoring workflow into separate functional segments, the system can apply specialized processing at each stage, reducing false positives from misconfigured resources while maintaining comprehensive health monitoring capability.
Solution Approach 2:
The patent implements feedback mechanisms where detection results are validated against multiple criteria and historical patterns before being classified as true anomalies. The system uses feedback loops to adjust detection thresholds and filter false positives, allowing reliable health monitoring while improving detection precision by learning from past false positive patterns.
Data Source
AI summary
Systems and methods for detecting an unstable resource of a cloud service. A set of health time-series data of a first resource is received and a resource behavior model trained on historical health time-series data of resources of a same type as the first resource is used to encode the received data into embeddings. In some examples, the model reconstructs the embeddings, compares the embeddings to the received data, and determines a reconstruction loss value for determining whether the first resource is operating in an anomalous behavior state. In some examples, the generated embeddings are compared to embeddings generated from health time-series data received from other resources of a same type as the first resource. A similarity-score is determined and used to determine whether the first resource is operating in an anomalous behavior state. The system and method further report anomalous behavior, indicating the first resource is unstable or unhealthy.