Invalid Event Detection in Distributed Computing via Learning Engine
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Solution Overview
Problem
In distributed computing environments, invalid or stale events can propagate, leading to inconsistent or incorrect data processing, causing system failures, network congestion, and inefficient resource use due to system failures or network latency.
Innovation Solution
A system that employs a learning engine to generate rule sets for each sub-system using machine learning, allowing it to detect and re-route invalid events, preventing further propagation and confirming their invalidity through metadata analysis and machine learning algorithms like decision tree learning and reinforcement learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If sub-systems continue to propagate events through the distributed computing environment, then event processing throughput is maintained, but invalid or stale events cause system failures, network congestion, and inefficient resource use
Solution Approach 1:
The patent applies preliminary action by checking event validity metadata (such as event age, source status, and validity flags) before propagating events through the distributed computing environment. This pre-validation mechanism prevents invalid events from entering the processing pipeline, thereby maintaining system reliability without sacrificing throughput. The learning engine continuously updates validity criteria based on historical data, enabling proactive filtering of potentially harmful events.
Solution Approach 2:
The patent introduces an intermediary validation layer between event generation and event processing. This intermediary component analyzes event metadata, compares it against learned patterns of valid events, and determines whether to allow propagation. This mediator prevents invalid events from causing system failures while maintaining legitimate event flow, thus resolving the contradiction between throughput and reliability.
2Measurement precision
If machine learning algorithms are used to detect invalid events, then accuracy in identifying invalid events is improved, but computational overhead and processing time increase
Solution Approach 1:
The patent applies partial action by using the learning engine to analyze only specific metadata fields of events (such as event age, source subsystem status, event type patterns) rather than performing comprehensive analysis of entire event data. This selective analysis approach achieves sufficient detection accuracy while minimizing computational overhead and processing time delays.
Solution Approach 2:
The patent uses copying by creating simplified representations or signatures of valid event patterns through the learning engine. Instead of performing complex real-time analysis of every event, the system compares events against pre-learned patterns or signatures, significantly reducing processing time while maintaining detection accuracy. This pattern-matching approach allows rapid validation without full computational overhead.
3Reliability
If event validity is checked at every sub-system, then propagation of invalid events is prevented, but network latency and operational resource usage increase
Solution Approach 1:
The patent applies segmentation by dividing the event validation responsibility across different components: the learning engine handles pattern learning and criteria development, while individual sub-systems handle local validation checks. This segmentation allows validity assurance to be distributed rather than centralized, reducing the computational burden on any single component and optimizing resource usage across the distributed environment.
Solution Approach 2:
The patent applies universality by creating a shared learning engine that serves multiple sub-systems with a common understanding of valid event patterns. This multi-functional approach allows all sub-systems to benefit from collective learning without each sub-system independently performing redundant analysis, thereby reducing overall operational resource usage while maintaining reliability across the entire distributed environment.
Data Source
AI summary
Described herein is a system for automatically detecting invalid events in a distributed computing environment. The system for automatically detecting invalid events may include sub-systems and a learning engine. The learning engine may generate a rule set for each sub-system specifying circumstances under which an event is considered invalid specific to the sub-system using machine learning. Sub-systems may detect an invalid event being propagated through the distributed computing environment based on a set of rules specifying circumstances under which an event is considered invalid specific to the sub-system and/or metadata of the event.