Cloud-Based Data Transfer Optimization via Machine Learning
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Solution Overview
Problem
Point-to-point data transfer systems rely on local information and static configurations, leading to inefficiencies and limitations in decision-making, as they lack global visibility and flexibility in adjusting transport mechanisms and parameters.
Innovation Solution
A cloud-based system that uses machine learning to determine optimal transfer parameters based on current network conditions, data set characteristics, and other information, allowing for dynamic adjustment of both transport and application layer parameters, and selection between multiple transport mechanisms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If local information and static configurations are used for data transfer decisions, then system simplicity is maintained, but decision-making efficiency and adaptability deteriorate
Solution Approach 1:
A cloud-based server acts as an intermediary between sending and receiving systems, collecting global visibility data and rendering transfer decisions. This mediator architecture resolves the contradiction by centralizing intelligence to improve decision-making efficiency while maintaining endpoint simplicity.
Solution Approach 2:
The system transitions from local, single-point decision-making to a distributed cloud-based decision architecture. By adding the cloud server dimension, the system achieves global visibility and adaptive decision-making without overwhelming local endpoints with complexity.
2Adaptability or versatility
If static configurations are used, then system stability is maintained, but adaptability to changing network conditions deteriorates
Solution Approach 1:
The system replaces static configurations with dynamic, adaptive decision-making. The cloud server continuously collects global visibility data and renders optimized transfer decisions in real-time, allowing the system to adapt to changing network conditions while maintaining reliable decision-making through data-driven approaches.
Solution Approach 2:
The cloud-based system implements feedback loops by collecting transfer results and global visibility data, analyzing them through machine learning models, and using the insights to improve future decisions. This feedback mechanism enhances both adaptability and reliability simultaneously.
3Adaptability or versatility
If single transport mechanism parameters are set, then configuration simplicity is maintained, but flexibility in optimization deteriorates
Solution Approach 1:
The cloud-based server provides universal decision-making capability that works across multiple transport mechanisms (TCP, UDP, QUIC). The system collects global visibility data and renders optimized decisions for any transport mechanism, eliminating the need for complex local configuration while maintaining flexibility and optimization capability.
Data Source
AI summary
Systems and methods to facilitate cloud-based point-to-point data transfer via machine learning are described herein. A request for a transfer of data between a sending system and a receiving system may be obtained. Receiving system information for the receiving system may be obtained. Values of transfer parameters for performing the transfer may be determined through machine-learning. The transfer may be performed based on the determined values. Results of the transfer may be obtained and provided to the machine-learning algorithm to further train the algorithm.