Distributed Software Service Anomaly Detection from Mixed Debugging Data
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Solution Overview
Problem
Distributed computing environments face challenges in efficiently analyzing mixed and disordered debugging data from multiple services, making it difficult to detect and debug functional anomalies due to dynamic service identifiers and complex execution sequences.
Innovation Solution
An anomaly detector parses mixed debugging data into service-specific groups using endpoint addresses, applies machine-learning models to analyze these groups, and generates alerts or mitigation actions based on predefined execution sequences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If debugging data from multiple services is collected and stored together, then comprehensive monitoring coverage is improved, but data organization and analysis complexity increases
Solution Approach 1:
The patent segments the mixed debugging data into service-specific groups using endpoint addresses as keys. Each service's debugging data is separated into its own group, allowing comprehensive monitoring of all services while maintaining organized, service-specific data structures that are easier to analyze individually.
2Productivity
If debugging data is analyzed without considering execution sequence, then analysis speed is improved, but detection accuracy of functional anomalies decreases
Solution Approach 1:
The patent performs preliminary action by establishing the expected execution sequence of services before analyzing debugging data. This pre-defined sequence serves as a reference framework that guides the anomaly detection process, allowing the system to quickly identify deviations from the expected flow while maintaining high accuracy in detecting functional anomalies.
3Adaptability or versatility
If service identifiers are made dynamic for flexibility, then system adaptability is improved, but data tracking and correlation difficulty increases
Solution Approach 1:
The patent introduces endpoint addresses as intermediary identifiers that remain stable even when service identifiers change dynamically. These endpoint addresses serve as consistent reference points for tracking and correlating debugging data across services, allowing the system to maintain reliable data tracking while preserving the flexibility of dynamic service identifiers.
Data Source
AI summary
Some examples can automatically detect functional anomalies associated with software services of a distributed application. For example, a system can receive a configuration file indicating an execution sequence for a set of services of a distributed application. The configuration file can include a mapping of a set of endpoint addresses to the set of services. The system can also receive a mixed set of debugging data associated with the set of services. The system can then parse the mixed set of debugging data into groups corresponding to the set of services based on the mapping in the configuration file. The system can then determine a sequence of debugging events by analyzing the groups, detect a functional anomaly associated with the set of services based on the sequence of debugging events, and generate an alert indicating the functional anomaly to a user.