Adaptive Data Pipeline for Edge Systems
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Solution Overview
Problem
Existing data distribution systems in edge systems face challenges in managing the collection and processing of diverse data types under conditions of varying network reliability and bandwidth, while ensuring efficient resource utilization and data security.
Innovation Solution
A distributed data pipeline architecture that adapts to changing conditions by classifying data based on type, criticality, and quantity, and selecting appropriate edge side processing methods such as immediate streaming or staging for future transactions, while ensuring secure connections and efficient data transmission.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If data is immediately streamed to processing systems, then data processing speed is improved, but network bandwidth consumption increases
Solution Approach 1:
The system changes the parameter of data transmission timing by classifying data into different categories (real-time, near-real-time, batch) and applying different transmission strategies to each category. This allows the system to optimize between speed and bandwidth consumption based on data priority and network conditions.
Solution Approach 2:
The data pipeline architecture dynamically adjusts data transmission and processing based on changing network conditions, data characteristics, and system load. The system can switch between immediate streaming and staged processing modes depending on real-time requirements.
2Device complexity
If diverse data types are processed through a unified system, then system complexity is reduced, but processing efficiency decreases
Solution Approach 1:
The system segments data processing into multiple specialized pipelines based on data type, criticality, and processing requirements. Each pipeline is optimized for specific data categories, improving processing efficiency while maintaining manageable system complexity through modular architecture.
Solution Approach 2:
Different processing methods and resources are allocated to different data types based on their specific requirements. Critical data receives priority processing with dedicated resources, while less critical data uses standard processing queues, optimizing overall system efficiency.
3Quantity of substance
If data is staged for future transactions, then network bandwidth usage is reduced, but data processing time increases
Solution Approach 1:
The system performs preliminary actions by pre-processing and staging data locally at edge systems before transmission. Data is prepared, validated, and buffered in advance, reducing the need for repeated network transmissions and minimizing delays when data is needed.
Solution Approach 2:
Edge systems act as intermediaries between data sources and central processing systems. They perform local data staging, filtering, and preprocessing, reducing network bandwidth usage while maintaining timely data availability through local buffering and intelligent transmission scheduling.
4Reliability
If secure connections are implemented for data transmission, then data security is improved, but transmission overhead increases
Solution Approach 1:
Security measures are segmented and applied selectively based on data classification. Different encryption and authentication protocols are applied to different data types and transmission channels, ensuring security for critical data while reducing overhead for less sensitive information.
Data Source
AI summary
Methods and systems for managing distribution of data are disclosed. The data may be distributed by edge systems to processing systems. The data may be distributed using a data pipeline. The data pipeline may include functionality to process data using different modalities depending on the type of data and operable connectivity between the edge systems and processing systems. By modifying the manner in which the data is processed, limitations in operable connectivity may be managed and a broader array of different types of data may be more efficiently distributed.