AI Data Transfer Strategy Optimization
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Solution Overview
Problem
Conventional systems for transferring massive amounts of data are cumbersome and time-consuming, requiring manual tuning and frequent adjustments to achieve optimal bandwidth, leading to increased operational expenses and potential sub-optimal performance.
Innovation Solution
The implementation of an AI-assisted flow optimization system that automatically creates and updates high-bandwidth data transfer strategies by obtaining and analyzing parameters from data sources, sinks, and network elements, and dynamically adjusting hardware and software settings to optimize data transfer.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual tuning and frequent adjustments are performed to achieve optimal bandwidth, then data transfer performance is improved, but operational complexity and time consumption increase
Solution Approach 1:
The system implements self-service through automated AI/ML-based optimization that independently tunes data transfer parameters without human intervention. The patent describes an automated system that monitors network conditions, analyzes performance metrics, and adjusts transfer strategies autonomously, eliminating the need for manual tuning while maintaining optimal bandwidth utilization.
Solution Approach 2:
The patent replaces manual mechanical tuning operations with automated computational systems using AI and machine learning algorithms. The system substitutes human operators with intelligent software agents that can rapidly analyze complex network parameters and optimize data transfer configurations without physical intervention or manual configuration.
2Adaptability or versatility
If manual tuning operations are performed frequently to adapt to changing network conditions, then data transfer optimization is improved, but operational expenses and man-hours increase
Solution Approach 1:
The system implements continuous feedback loops where performance metrics from data transfers are collected, analyzed by AI/ML models, and used to automatically adjust transfer parameters. This closed-loop control enables the system to adapt to changing network conditions in real-time without requiring manual re-tuning, achieving high adaptability while minimizing operational time and resources.
Solution Approach 2:
The patent employs preliminary action by pre-configuring automated optimization systems and AI models that are ready to immediately respond to network condition changes. The system establishes proactive monitoring and pre-programmed response strategies that enable rapid adaptation without waiting for manual intervention, reducing both time consumption and operational expenses.
3Reliability
If manual processes are used to tune DTN systems, then system optimization is achieved, but scalability becomes difficult
Solution Approach 1:
The patent implements universality through a standardized automated optimization platform that can be deployed across multiple DTN systems and network configurations. The AI/ML-based system provides multi-functional capabilities that work consistently across different scales and environments, enabling reliable optimization that scales from single-node to multi-node deployments without requiring proportional increases in manual operational resources.
Data Source
AI summary
Systems and methods for creating and updating data transfer strategies for massive amounts of data are provided. A method, according to one implementation, includes obtaining a first set of parameters pertaining to a data source configured as a first node in a communications system, obtaining a second set of parameters pertaining to a data sink configured as a second node in the communications system, and obtaining a third set of parameters pertaining to a plurality of network elements and links configured along one or more data paths between the data source and data sink. The method also includes the step of automatically creating a high-bandwidth data transfer strategy for transferring a massive amount of data from the data source to the data sink based on the first, second, and third sets of parameters.