Elastic Bulk Data Processing System for Real-Time Network Monitoring
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Solution Overview
Problem
Conventional data processing systems face challenges in real-time data collection and processing due to high latency, handling of heterogeneous data from multiple vendors with different formats and transmission mechanisms, and inefficient data distribution to downstream applications, leading to data duplication and storage inefficiencies.
Innovation Solution
A system with an elastic architecture and flexible ingestion layer that adapts to various data formats and transport mechanisms, employing a pub/sub data distribution mechanism to process and distribute data in real-time, ensuring scalability and minimizing latency, and harmonizing data from different vendors into a uniform format for downstream consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If conventional file transfer mechanism is used for data collection, then data can be collected from multiple sources, but end-to-end latency is high and real-time processing is not achieved
Solution Approach 1:
The patent replaces the mechanical file transfer system with a streaming data collection system using publish-subscribe architecture. Data is collected continuously via streaming protocols (UDP, TCP, file-based streaming) and processed in real-time through a standardized interface layer, eliminating the batch processing delays inherent in conventional file transfer mechanisms.
Solution Approach 2:
The patent introduces a standardized data collection interface layer as an intermediary between diverse data sources and downstream applications. This layer receives data in various formats (ASN.1, Protocol Buffers, proprietary binary), standardizes it, and distributes it via pub/sub mechanism, enabling real-time processing while maintaining compatibility with multiple vendors' data formats and transmission mechanisms.
2Adaptability or versatility
If data from multiple vendors with different formats is collected using conventional systems, then comprehensive data coverage is achieved, but data duplication and storage inefficiencies occur
Solution Approach 1:
The patent applies homogeneity by standardizing all incoming data from different vendors into a uniform data model at the collection interface layer. All data is transformed into a common format with standardized schemas, enabling downstream applications to consume data consistently without requiring multiple format-specific processing paths, thereby eliminating redundant storage of the same data in multiple formats.
Solution Approach 2:
The standardized data collection interface serves multiple functions: it collects data from diverse sources, validates and standardizes formats, performs initial processing, and distributes data to multiple downstream applications simultaneously. This multi-functional approach replaces multiple vendor-specific collection systems with a single universal interface, reducing storage redundancy.
3Productivity
If elastic architecture with horizontal scaling is implemented, then system capacity increases, but device complexity increases
Solution Approach 1:
The patent segments the data processing system into distinct modular layers: data collection layer (ingesting from network elements), standardization layer (transforming to uniform format), pub/sub distribution layer (routing to subscribers), and application layer (downstream processing). Each layer operates independently and can be scaled horizontally by adding more instances of that specific layer, increasing capacity while managing complexity through clear separation of concerns.
Data Source
AI summary
The system allows real time collection and processing of massive data from many network elements. It has an elastic architecture that scales horizontally to support different network sizes. The system in a uniform data format for downstream consumption. It employs a pub/sub data distribution mechanism that supports multiple concurrent downstream subscribers efficiently in real-time.