DAG Workflow Decomposition for Cloud Platform Management
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Solution Overview
Problem
Cloud-based platforms require extensive training to manage effectively, and no-code/low-code development platforms face challenges with testing, processing efficiency, ownership identification, and operational optimization due to their complexity and lack of cross-compatibility.
Innovation Solution
The technology decomposes directed acyclic graph (DAG) workflows into simple time-affecting linear pathways (STALPs) and further breaks down no-code/low-code processes into markup or scripting language time-affecting linear pathways (M-S TALPs) for enhanced management and analytics, minimizing human errors and optimizing resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If cloud-based platforms are managed manually, then users have direct control over platform hardware, software, security systems, and access restrictions, but it requires extensive training and skill (e.g., day-long courses plus multiple five-day courses and fifty-seven days of required training for Azure alone)
Solution Approach 1:
The patent segments complex cloud platform management into discrete, automated workflow tasks organized as directed acyclic graphs. Each node represents a specific management action, breaking down the complex training requirement into manageable, automated steps that execute without extensive human expertise.
Solution Approach 2:
The system enables self-service platform management through automated workflows that execute management tasks independently. The DAG-based automation performs security system configuration, access restriction management, and resource allocation without requiring manual intervention or extensive training, allowing users to leverage pre-defined automated processes.
2Ease of manufacture
If no-code/low-code development platforms are used, then non-programmers can create application programs through drag-and-drop processes, but adequate testing, processing efficiency, ownership identification, and operational optimization become concerns
Solution Approach 1:
The patent implements feedback mechanisms that automatically test no-code/low-code workflows and provide optimization recommendations. The system analyzes DAG execution results, identifies bottlenecks, and suggests process improvements, ensuring reliable testing and continuous optimization without requiring programming expertise.
Solution Approach 2:
The system automatically adjusts workflow parameters such as execution timing, resource allocation, and process sequencing based on analyzed performance data. By dynamically modifying DAG parameters, the system optimizes processing efficiency and ensures reliable operation while maintaining the simplicity of no-code/low-code creation.
3Productivity
If cloud-based platforms are managed using automated workflows, then processing efficiency improves, but the complexity of managing multiple platforms increases due to lack of cross-compatibility
Solution Approach 1:
The patent creates a universal DAG workflow framework that can manage multiple cloud platforms through standardized abstract representations. The same DAG-based automation principles apply across different platforms, enabling cross-compatibility while maintaining processing efficiency. Platform-specific details are abstracted away, allowing unified management of heterogeneous cloud environments.
Data Source
AI summary
Systems and methods are presented that automatically construct a directed acyclic graph (DAG) workflow used to control and manage algorithms on multiple, disparate, cloud-based development and deployment platforms. DAG workflows are comprised of a set of simplified, fixed time-affecting linear pathways (STALPs). Algorithms are constructed using no-code/low-code methods that are then automatically decomposed into a set of markup or scripting language time-affecting linear pathways (M-S TALPs). Prediction polynomials that approximate advanced time and space complexity functions are created using M-S TALPs and are used for M-S TALP identification, optimization, efficiency, and performance enhancement on selected computing platforms.