AI-Based Directed Acyclic Graph Management System
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Solution Overview
Problem
Manual management of Directed Acyclic Graphs (DAGs) in software development workflows is inefficient, particularly in complex projects, as it relies solely on manual user inputs.
Innovation Solution
An AI-based automated system for real-time management of DAGs using predictive analytics on DAG input-related data, which includes a processor configured to host a machine learning module, acquire and parse data, query historical databases, generate feature vectors, and provide inputs for generating predictive models to update DAG parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual user inputs are used to manage DAGs, then the system is simple to operate, but productivity is low and efficiency is poor in complex software development workflows
Solution Approach 1:
The system enables automated DAG management where the AI model autonomously generates optimization recommendations and updates DAG parameters without requiring manual user input. The system self-learns from historical execution data and automatically adjusts task scheduling, resource allocation, and pipeline optimization based on predictive analytics.
Solution Approach 2:
The patent replaces manual mechanical operations with an AI-based automated system. Machine learning models analyze historical DAG execution data, generate predictive insights, and automatically optimize DAG parameters, substituting human manual management with intelligent automated decision-making processes.
2Productivity
If automated AI-based management is implemented, then productivity and efficiency are improved, but device complexity increases due to ML modules and predictive analytics
Solution Approach 1:
The AI-based management system serves multiple functions: it analyzes historical execution data, predicts future DAG performance, generates optimization recommendations, and automatically updates DAG parameters. This multi-functional system consolidates what would otherwise require separate manual processes into a single automated platform.
Solution Approach 2:
The system continuously collects feedback from historical DAG execution data, uses this feedback to train and improve predictive models, and applies learned insights to automatically optimize DAG parameters. This closed-loop feedback mechanism enables the system to continuously improve productivity while managing complexity through data-driven decision-making.
3Measurement precision
If real-time predictive analytics are used, then DAG management accuracy is improved, but loss of time increases due to data processing and model generation
Solution Approach 1:
The system performs preliminary actions by continuously training predictive models on historical data in advance and maintaining ready-to-use optimization strategies. When new DAG execution data arrives, the pre-trained models can quickly generate predictions without requiring extensive real-time computation, thus reducing data processing time while maintaining high accuracy.
Solution Approach 2:
The predictive analytics system dynamically adjusts its processing based on data availability and computational resources. The model training and prediction processes are designed to be adaptive, scaling computation based on urgency and available resources, allowing the system to balance accuracy requirements with time constraints in real-time operations.
Data Source
AI summary
A system for an automated real-time management of a directed acyclic graph (DAG) based on predictive analytics of DAG input-related data including a processor of a graph compute manager (GCM) node configured to host a machine learning (ML) module and connected to at least one DAG source entity node over a network and a memory on which are stored machine-readable instructions that when executed by the processor, cause the processor to: acquire the DAG input-related data from the at least one DAG source entity node; parse the DAG input-related data to derive a plurality of key features; query a local DAGs' database to retrieve local historical DAGs'-related data associated with previous DAG parameters based on the plurality of key features; generate at least one feature vector based on the plurality of key features and the local historical DAGs'-related data; and provide the at least one feature vector to the ML module for generating a predictive model configured to produce at least one DAG update parameter for updating the DAG at the at least one DAG source entity.