Geographic Data Storage Hops for Asynchronous Network Transfer
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Solution Overview
Problem
When sending and receiving large data sets across different time zones, existing methods face congestion issues due to asynchronous bandwidth availability, leading to suboptimal transmission rates and longer transfer times.
Innovation Solution
The implementation of a system that uses storage-hops strategically positioned across different geographical locations to route data during low-traffic hours, utilizing an algorithm to construct a flow network that maximizes bandwidth utilization and minimizes congestion by determining the optimal number and placement of storage-hops.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If data transmission is scheduled for early morning hours when bandwidth availability is high, then transmission rate is improved, but receiver congestion increases when sender and receiver are in different time zones
Solution Approach 1:
The patent introduces intermediate storage nodes positioned in different time zones that act as mediators between sender and receiver. These intermediaries receive data during low-congestion periods and forward it during their own low-congestion periods, enabling asynchronous data transfer across time zone boundaries without direct congestion conflicts.
Solution Approach 2:
The data transmission path is segmented into multiple hops across different time zones rather than a direct single connection. Each segment operates during its local low-congestion hours, breaking the synchronous congestion problem into multiple asynchronous segments that can proceed independently.
2Loss of time
If direct data transfer is used between sender and receiver, then device complexity is reduced, but transfer time increases due to network congestion
Solution Approach 1:
Data is prepared and staged at intermediate storage nodes during their low-congestion periods before the actual transmission to the final destination. This preliminary action allows the system to pre-position data segments that will be forwarded during optimal windows, reducing overall transfer time despite the added complexity of multiple nodes.
3Productivity
If storage-hops are added to route data during low-traffic hours, then transmission efficiency is improved, but system complexity increases
Solution Approach 1:
The system dynamically selects and activates storage hops based on real-time network conditions and time zone differences. Rather than maintaining a fixed complex network, the routing path adapts dynamically to optimize for low-congestion windows, improving efficiency while keeping the active network path relatively simple.
Data Source
AI summary
Techniques are provided for generating data on the optimum number of storage-hops, the location of the storage-hops, and the bandwidth distributions of the storage-hops to construct a complete flow network for transmitting data from a sender to a receiver via a communication network. The flow network can potentially include hundreds of storage-hops, depending on the time and duration of the data flow. An algorithm is further provided for constructing an unbounded flow network from a bounded set of input parameters. Moreover, the complexity of the algorithm does not depend on the number of storage-hops, so the model is suitable for both crowd supported and data center supported transfers.