Intermediate Tunnel Nodes for Slice-Based Internet Content Fetching
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Solution Overview
Problem
Existing Internet communication technologies face inefficiencies due to network congestion, traffic load balancing, and unpredictable network behavior, leading to issues such as packet loss, duplication, and out-of-order delivery, which are not effectively addressed by current TCP/IP protocols.
Innovation Solution
Implementing intermediate nodes that can function as both end-user and intermediate devices, partitioning data into slices for transmission through multiple tunnel devices, and reconstructing the content at the client device, allowing for improved data delivery and error handling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If TCP/IP protocols are used for Internet communication, then standardization and compatibility are improved, but network congestion and packet loss occur due to unpredictable network behavior
Solution Approach 1:
The patent introduces an intermediary communication system that sits between the application layer and TCP/IP stack. This intermediary monitors network conditions, predicts packet loss using machine learning models, and proactively adjusts transmission parameters before actual loss occurs, thereby improving reliability while maintaining protocol compatibility
Solution Approach 2:
The system performs preliminary actions by predicting network congestion and packet loss before they actually occur. Using historical network data and machine learning algorithms, the system anticipates potential issues and adjusts transmission strategies in advance, preventing packet loss rather than reacting to it afterward
2Device complexity
If traditional communication systems are used, then simplicity is maintained, but resource utilization and response times are suboptimal
Solution Approach 1:
The communication system performs self-service by automatically monitoring its own performance, predicting issues, and adjusting its behavior without external intervention. The machine learning models continuously learn from network data and autonomously optimize transmission parameters, improving productivity while adding minimal complexity
Solution Approach 2:
The system implements continuous feedback loops where network performance data is collected, analyzed by machine learning models, and used to adjust transmission parameters in real-time. This feedback mechanism enables the system to optimize resource utilization and response times dynamically without requiring complex manual configuration
Data Source
AI summary
A method for fetching a content from a web server to a client device is disclosed, using tunnel devices serving as intermediate devices. The client device accesses an acceleration server to receive a list of available tunnel devices. The requested content is partitioned into slices, and the client device sends a request for the slices to the available tunnel devices. The tunnel devices in turn fetch the slices from the data server, and send the slices to the client device, where the content is reconstructed from the received slices. A client device may also serve as a tunnel device, serving as an intermediate device to other client devices. Similarly, a tunnel device may also serve as a client device for fetching content from a data server. The selection of tunnel devices to be used by a client device may be in the acceleration server, in the client device, or in both. The partition into slices may be overlapping or non-overlapping, and the same slice (or the whole content) may be fetched via multiple tunnel devices.