Semantic Reasoning Engine for Automated Network Workflow Validation
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Solution Overview
Problem
Network administrators face challenges in troubleshooting complex networks due to the inflexibility of existing tools, which require profound knowledge and coding skills, making it time-consuming to run different configurations and test workflows effectively.
Innovation Solution
A semantic machine reasoning engine is used to automate the testing and validation of network-based workflows through a workflow editor interface, allowing users to create workflows without coding, using a drag-and-drop interface and compiling them into Web Ontology Language (OWL) files for interpretation by the engine, which performs inference and validation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If traditional data templates and manual configuration methods are used, then network provisioning can be automated to some extent, but the system requires profound network knowledge and coding skills, significantly increasing operational complexity and time consumption
Solution Approach 1:
The system enables self-service automation by allowing the network system itself to generate, validate, and execute configuration workflows automatically. The semantic machine reasoning engine autonomously processes natural language inputs, generates appropriate network configurations, and validates them without requiring user coding skills or deep network knowledge, thus achieving both high automation and ease of operation
Solution Approach 2:
The semantic machine reasoning engine acts as an intermediary between the user's natural language intent and the network configuration system. It translates high-level user requirements into precise technical configurations, bridging the gap between simple user interaction and complex network automation, thereby eliminating the need for users to possess specialized knowledge while maintaining full automation capability
2Reliability
If comprehensive network configurations and multiple test scenarios are executed, then workflow validation thoroughness is improved, but the time required to run different configurations and validate workflows is significantly prolonged
Solution Approach 1:
The system performs preliminary validation by generating synthetic test data and executing validation scenarios automatically during the workflow creation phase. The semantic machine reasoning engine pre-checks configuration validity, identifies potential issues, and validates workflows against predefined criteria before actual deployment, ensuring thorough validation without requiring time-consuming manual testing of multiple configurations
Solution Approach 2:
The system replaces manual mechanical testing processes with automated semantic reasoning and validation. Instead of requiring users to manually execute multiple test configurations and scenarios, the semantic machine reasoning engine automatically generates test cases, executes them programmatically, and validates results, thereby maintaining comprehensive validation thoroughness while dramatically reducing the time required
3Adaptability or versatility
If users are required to write codes and scripts for running configurations, then precise control over network tasks is achieved, but the system becomes inaccessible to users without programming expertise, reducing usability
Solution Approach 1:
The semantic machine reasoning engine serves as an intelligent intermediary that translates natural language user intent into precise configuration commands and scripts. Users can express their requirements in everyday language while the engine generates the exact technical code and scripts needed for precise network control, thereby maintaining configuration precision while eliminating the need for user programming skills
Solution Approach 2:
The system substitutes the manual coding process with automated semantic code generation. Instead of requiring users to write code, the semantic machine reasoning engine automatically generates precise configuration scripts and commands based on natural language inputs, thereby maintaining precise control over network tasks while making the system accessible to non-programmers
4Ease of operation
If flexible workflow creation without coding is enabled, then ease of operation is improved, but the system requires advanced semantic reasoning and automated validation capabilities, increasing device complexity
Solution Approach 1:
The system extracts and isolates the complex semantic reasoning engine as a separate, dedicated component from the user-facing workflow creation interface. This allows the complex reasoning and validation logic to be contained in a specialized module, keeping the user interface simple and intuitive while housing the advanced capabilities in a separate engine that can be independently managed and optimized
Solution Approach 2:
The semantic machine reasoning engine acts as an intermediary layer between the simple user interface and the complex network configuration system. It absorbs and manages the complexity of semantic understanding, workflow validation, and configuration generation, presenting a simplified interface to users while handling all the sophisticated processing internally, thereby enabling ease of operation without exposing device complexity
Data Source
AI summary
The present technology addresses a need in the art for an automated tool that allows users to create network-based custom workflows for networks and associated management applications. The users do not need to have in-depth network knowledge to work with the tool or even write any code/script. The tool provides the users with a flexible graphical user interface for automated troubleshooting, network provisioning, and closed-loop automation. Further, the tool uses a domain-independent semantic machine reasoning engine as an underlying engine and a mock data engine to test and validate network-based workflows created by the users.