Self-localizing Data Distribution Network for Reducing Backhaul Transfer Costs
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Solution Overview
Problem
Large-scale distributed data processing systems face limitations due to high data transfer costs between nodes, which hinder the efficiency of network monitoring and data distribution, especially in telecommunications networks where data often needs to be processed and stored across geographically dispersed nodes.
Innovation Solution
The system adaptively self-localizes data processing by modifying ownership association values based on access history, transferring ownership of data from the current owner node to the second most-frequently accessing node when a selected cutoff is reached, thereby minimizing backhaul data transfer costs and optimizing data processing proximity to its source.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data is distributed across geographically dispersed nodes to handle big data challenges, then computational and storage resources are increased, but data transfer costs between nodes increase
Solution Approach 1:
The patent implements dynamic data ownership transfer where the system continuously monitors access patterns and automatically transfers data ownership between nodes. This dynamic adaptation allows the system to optimize data distribution in real-time based on actual usage, resolving the contradiction between distributed resource access and data transfer costs by moving data closer to where it is needed most.
Solution Approach 2:
The system performs self-optimization through automatic ownership transfer based on access history. The distributed database autonomously identifies when a node should own data based on access patterns and executes transfers without external intervention, enabling the system to self-adjust and minimize transfer costs while maintaining efficient resource utilization.
2Device complexity
If data ownership is statically assigned to nodes, then system simplicity is maintained, but data processing efficiency decreases due to unnecessary data transfers
Solution Approach 1:
The patent implements a feedback mechanism where access history is continuously monitored and used to adjust data ownership assignments. The system uses feedback from access patterns to dynamically transfer ownership, creating a closed-loop system that automatically optimizes data distribution based on actual usage without requiring complex manual configuration.
Solution Approach 2:
The system changes the parameter of data ownership assignment from static to dynamic based on access history. By monitoring access patterns and adjusting ownership parameters in real-time, the system transforms rigid data distribution into flexible, demand-driven distribution that optimizes processing efficiency while maintaining manageable system complexity through automated parameter adjustment.
Data Source
AI summary
To adaptively self-localize distributed data processing and data distribution and reduce data transfer costs in a network monitoring system, data has a corresponding ownership association. For each data access, an ownership association value for the accessed data may be modified based on whether the access originated with a current owner processing node or a second most-frequently accessing processing node. The ownership association value indicates a strength of the ownership association between the data and the owner and is based on at least a recent history of accesses of the data by the current owner and the second most-frequently accessing node. When the ownership association value traverses a selected cutoff, ownership association of the data is transferred from the current owner to the second most-frequently accessing node. The ownership association transfer contributes to self-localizing data processing based on a source of input regarding the data.