ADNA Workflow Blocks for Decentralized Exception Handling
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Solution Overview
Problem
Existing 5G network technologies face challenges in efficiently managing decentralized workflows and network slices due to the unpredictability of sub-processes and tasks, leading to increased transaction overhead and system downtime.
Innovation Solution
Implementing a dynamically scalable ADNA Manager with decentralized atomic decision making, utilizing ADNA task blocks that execute workflow rules without a fixed orchestration, and employing exception ADNAs for handling unsatisfied input parameters, reducing transactions and enhancing system performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If decentralized atomic decision making is implemented using ADNA task blocks, then system scalability and productivity are improved, but device complexity increases due to the dynamic pool of task blocks and exception handling mechanisms
Solution Approach 1:
The workflow system is segmented into atomic deterministic next action (ADNA) task blocks, each representing a discrete, independently executable unit of work. This segmentation allows the system to process workflows in manageable atomic units that can be dynamically selected and executed, improving scalability while maintaining manageable complexity through clear modular boundaries
Solution Approach 2:
An ADNA manager acts as an intermediary component that orchestrates the selection, execution, and coordination of ADNA task blocks. This mediator manages the pool of task blocks, handles exception ADNAs, and coordinates workflow progression, thereby reducing the complexity burden on individual task blocks while enabling scalable workflow management
2Reliability
If exception ADNAs are invoked frequently due to unsatisfied input parameters, then measurement precision and reliability are improved through validation, but loss of time increases due to repeated exception handling
Solution Approach 1:
Input parameter validation is performed preliminarily before ADNA task block execution through qualification rules that check parameters in advance. This preliminary validation ensures that only properly validated parameters proceed to execution, preventing repeated exception handling and reducing time loss while maintaining high reliability through upfront verification
Solution Approach 2:
The system implements feedback mechanisms where exception ADNAs provide information about unsatisfied qualification rules back to the workflow orchestrator. This feedback enables the system to learn from validation failures, adjust parameter passing, and prevent repeated exceptions, thereby reducing time loss while maintaining rigorous validation standards
Data Source
AI summary
Methods and apparatuses for providing a dynamically scalable ADNA Manager with decentralized atomic decision making are described. The decentralized atomic decision making may be performed using atomic deterministic next action (ADNA) task blocks that execute one or more workflow rules and then invoke one or more ADNAs within a pool of ADNAs. The ADNA Manager may identify a first ADNA task block, determine a set of input parameters for the first ADNA task block, detect that a first input parameter of the set of input parameters does not satisfy a qualification rule for the first ADNA task block, identify an exception ADNA task block in response to detection that the first input parameter does not satisfy the qualification rule, store breadcrumb information for the first ADNA task block within a persistence layer prior to the exception ADNA task block being invoked, and invoke the exception ADNA task block.