Virtualization Pattern Anomaly Detection in Cloud Environments
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Solution Overview
Problem
Anomaly detection in cloud environments is challenging due to the complexity of multiple tenants using various services, making it difficult to identify and respond to breaches effectively.
Innovation Solution
A system that learns virtualization patterns by monitoring virtual processes and resources, using machine learning algorithms to compare current patterns to baseline data, and taking actions such as quarantining or unloading anomalous processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional anomaly detection methods are used in cloud environments, then the system can detect some anomalies, but the detection precision deteriorates due to the complexity of multiple tenants and services
Solution Approach 1:
The patent segments the cloud environment into isolated virtual process groups, each monitored independently. By dividing the complex multi-tenant cloud system into separate virtual process segments, the system can apply anomaly detection to each segment individually, improving precision while managing complexity through modular analysis rather than attempting to analyze the entire cloud environment as a single system.
2Reliability
If comprehensive monitoring of all virtual processes is implemented, then anomaly detection capability improves, but the computational resources and system complexity increase
Solution Approach 1:
The patent merges the monitoring of multiple virtual processes into unified process groups that share common characteristics or relationships. By combining related virtual processes into single monitoring units, the system achieves comprehensive coverage of all processes while reducing the overall complexity of the monitoring infrastructure through consolidation and shared analysis mechanisms.
3Speed
If real-time analysis of virtualization patterns is performed, then the speed of anomaly identification improves, but the computational energy consumption increases
Solution Approach 1:
The patent performs preliminary analysis by pre-establishing baseline virtualization patterns and process group characteristics before actual anomaly detection occurs. By preparing reference models, typical behavior profiles, and grouping relationships in advance, the system can perform rapid real-time comparison during operation, achieving fast anomaly identification without the computational burden of analyzing all parameters from scratch during each monitoring cycle.
Data Source
AI summary
A virtualization pattern is learned. The learned virtualization pattern comprises information associated with one or more virtualized processes. For example, the virtualization pattern may comprise the creation of a first micro service and the spawning of a second micro service from the first micro service. The learned virtualization pattern is compared to a current virtualization pattern to identify an anomalous virtualization pattern in the current virtualization pattern. In response to identifying the anomalous virtualization pattern in the current virtualization pattern, an action is taken. For example, the action taken may be to quarantine a virtual process, to unload the virtual process, to quarantine a tenant partition, and/or the like.