CloudSeer Real-Time Cloud Log Error Detection
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Solution Overview
Problem
Conventional systems face challenges in detecting subtle errors and performance issues in cloud infrastructure due to complexity and non-determinism, often requiring offline analysis and assuming unique log message identifiers, which can lead to delayed error detection and increased overhead.
Innovation Solution
The CloudSeer system generates models from training execution logs to represent correct task executions, allowing for real-time error detection by analyzing live logs without additional overhead, using automata to check log message sequences for anomalies and providing execution context for error correction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional offline analysis is used with log messages, then error detection can be performed, but detection speed is slow and requires waiting for all logs to be collected
Solution Approach 1:
The system performs preliminary training by executing training tasks and collecting training logs before actual operation. During this phase, it learns the normal execution patterns and builds detection models in advance. When real tasks execute, the pre-built models enable immediate online detection without waiting for log collection, thus resolving the time delay while maintaining detection accuracy.
2Ease of operation
If unique identifiers are assumed in log messages for grouping, then task instance differentiation is enabled, but the system fails when identifiers are missing or non-unique
Solution Approach 1:
The system introduces task context information as an intermediary element between log messages and their grouping. Instead of relying solely on unique identifiers, it uses contextual features from the learned models (such as sequence patterns, temporal relationships, and execution characteristics) to associate log messages with task instances. This intermediary approach enables reliable grouping even when identifiers are missing or non-unique.
3Measurement precision
If code instrumentation is used to determine vector clocks and happens-before relations, then temporal ordering is achieved, but execution overhead is added
Solution Approach 1:
The system uses self-service by leveraging the existing log messages generated during normal task execution. Instead of adding instrumentation code that modifies the executing platform, it analyzes the logs produced by the system's own operations to infer temporal relationships and build detection models. This approach achieves precise temporal ordering without adding execution overhead.
4Reliability
If offline model creation is performed for all task instances, then comprehensive error detection is possible, but the process is complex and resource-intensive
Solution Approach 1:
The system segments the error detection process into two distinct phases: training phase and detection phase. During training, it processes multiple task executions to learn patterns and build generalized detection models. During detection, it applies these pre-built models to new tasks. This segmentation reduces complexity by separating the computationally intensive model-building step from the lightweight online detection step.
Data Source
AI summary
Systems and methods are disclosed for detecting error in a cloud infrastructure by running a plurality of training tasks on the cloud infrastructure and generating training execution logs; generating a model miner with the training execution logs to represent one or more correct task executions in the cloud infrastructure; after training, running a plurality of tasks on the cloud infrastructure and capturing live execution logs; and from the live execution logs, if a current task deviates from the correct task execution, indicating an execution error for correction in real-time.