Network Fault Injection via Dependency-Based Scenario Simulation
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Solution Overview
Problem
Existing methods for simulating fault injections in network infrastructures are time-consuming, error-prone, and inefficient, particularly in complex modern environments, often requiring human intervention and lacking scalability.
Innovation Solution
An automated system using a computing device that scans a network infrastructure, collects metadata and historical data, and employs a computer model to recommend fault injection scenarios based on dependency protocols and attributes, allowing for automated determination of fault points and simulation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual fault injection methods are used, then human intervention and deep understanding of service dependencies are required, but the process becomes time-consuming and error-prone
Solution Approach 1:
The system enables self-service fault injection by automatically analyzing service dependencies and generating fault injection scenarios without requiring manual human intervention. The automated dependency graph analysis and fault scenario generation eliminate the need for operators to manually identify injection points and configure fault parameters, thereby reducing time consumption while maintaining high reliability through systematic coverage of critical failure modes
Solution Approach 2:
The system performs preliminary actions by pre-analyzing service dependencies and pre-generating fault injection scenarios before actual fault injection occurs. The dependency graph is built in advance, and potential fault scenarios are identified and configured beforehand, allowing for rapid execution of comprehensive fault injection tests without requiring deep real-time analysis during the testing process
2Reliability
If comprehensive fault injection testing is performed, then testing coverage and reliability improve, but the complexity and time required for manual configuration increases
Solution Approach 1:
The system automatically generates comprehensive fault injection scenarios by analyzing the service dependency graph, eliminating the need for manual configuration of complex fault parameters. The automated system identifies critical dependencies and generates appropriate fault scenarios based on the analyzed relationships between services, thereby achieving comprehensive testing coverage without increasing operational complexity
Solution Approach 2:
The system segments the complex fault injection process into distinct automated stages: dependency graph construction, critical dependency identification, and fault scenario generation. This segmentation allows each component to be handled independently and automatically, reducing the overall complexity of configuring comprehensive fault injection tests while maintaining thorough testing coverage
3Productivity
If automated fault injection is implemented, then efficiency and scalability improve, but the need for sophisticated computer models and data collection increases system complexity
Solution Approach 1:
The system introduces an intermediary dependency analysis module that sits between the service infrastructure and the fault injection mechanism. This intermediary layer automatically collects metadata, builds dependency graphs, and generates fault scenarios, thereby simplifying the overall system architecture by encapsulating complexity in a dedicated layer rather than distributing it throughout the entire system
Data Source
AI summary
A system may include a network infrastructure having a set of network component nodes, each network component node configured to communicate with at least one other network component node in accordance with a dependency protocol; and a server in communication with the network infrastructure and a fault injection server. The server can be configured to monitor outputs generated by the network infrastructure and attributes of data communication between the set of network component nodes; execute a computer model using the dependency protocol and the monitored attributes and outputs as input to predict a set of faults; in response to presenting the set of faults for display on a user interface, receive a selection of one or more of the set of faults; and instruct the fault injection server to execute a fault injection scenario simulating performance of the network infrastructure operating under the selected one or more faults.