Predictive Failure Analysis for Dynamic Error Injection
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Solution Overview
Problem
Existing software development techniques struggle to anticipate and handle runtime errors and exceptions in complex, distributed systems due to limitations in design-time error and exception handling methods, which become insufficient as systems evolve and become more complex.
Innovation Solution
A predictive failure analysis system that dynamically injects error detection and handling logic into software routines, enabling systems to respond to potential failures without requiring developers to anticipate errors at design time, using machine learning algorithms to analyze event streams and generate predictive failure trends and responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If design-time error and exception handling techniques are used, then software reliability can be improved for anticipated errors, but the system cannot handle unexpected runtime errors in complex distributed systems
Solution Approach 1:
The system performs preliminary actions by analyzing event streams and generating failure predictions before actual failures occur. The predictive failure analysis system processes historical and real-time events to identify patterns that indicate upcoming failures, allowing the system to prepare and respond proactively rather than reactively.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring event streams and using the observed events to refine failure predictions. The predictive model learns from actual system behavior and adjusts its predictions based on feedback from real-time event analysis, improving its ability to anticipate failures in complex distributed systems.
2Reliability
If dynamic error detection logic is injected into software routines, then the system can respond to potential failures in real-time, but the complexity of the system increases
Solution Approach 1:
The system introduces an intermediary predictive failure analysis component that sits between the event sources and the error handling logic. This intermediary analyzes events and generates failure predictions without requiring direct modification of existing software routines, thereby adding predictive capability while maintaining a degree of separation and reducing overall system complexity.
Solution Approach 2:
The system replaces traditional mechanical error handling approaches (explicit exception declarations and handlers) with a data-driven predictive model. Instead of relying on manually configured error handling mechanisms, the system uses machine learning algorithms to automatically predict and respond to failures, reducing the complexity of manual error handling configuration.
3Ease of manufacture
If traditional exception handling is used in library-based applications, then development is simpler with explicit exception declarations, but service-oriented architectures and micro-services cannot adequately anticipate runtime errors
Solution Approach 1:
The predictive failure analysis system provides a universal solution that works across different architectural styles including library-based applications, service-oriented architectures, and micro-services. The system can analyze events from any source and generate predictions applicable to various system types, eliminating the need for architecture-specific error handling approaches.
Solution Approach 2:
The system enables self-service error handling by automatically analyzing events and generating failure predictions without requiring developers to explicitly declare or handle every possible exception. The predictive model autonomously identifies potential failures and provides guidance for error handling, reducing the development burden while improving reliability.
Data Source
AI summary
Embodiments described herein provide a predictive failure analysis that enables design-time error and exception handling techniques to be supplemented or assisted by a predictive failure analysis system. One embodiment provides an electronic device, comprising a non-transitory machine-readable medium to store instructions; one or more processors to execute the instructions; and a memory coupled to the one or more processors, the memory to store the instructions which, when executed by the one or more processors, cause the one or more processors to receive injection of dynamic error detection logic into the instructions, the dynamic error handling logic including an error handling update to indicate a response to a predicted failure; receive a set of events indicative of the predicted failure; and respond to the set of events according to the error handling update.