Distributed Network Analytics Agents for Low-Overhead Data Processing
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Solution Overview
Problem
Existing network analytics approaches involve significant overhead in data collection and transfer, inefficiencies in data utilization, and limitations in scalability due to centralized data processing, leading to bottlenecks and increased costs.
Innovation Solution
A distributed network analytics framework that distributes analytics tasks to network nodes, using agents and controllers to configure data sources, minimize data generation, and reduce transfer overhead, allowing for real-time processing and dynamic adjustment of analytics tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If centralized data collection is used for network analytics, then data processing can be performed uniformly, but network overhead and costs increase significantly
Solution Approach 1:
The patent segments the centralized analytics system into distributed analytics agents deployed at individual network nodes. Each agent independently performs analytics functions locally, eliminating the need to centralize all data collection and processing. This segmentation reduces network overhead while maintaining processing capabilities through distributed execution of analytics tasks across multiple nodes.
2Quantity of substance
If all network data is collected and transferred centrally, then complete data availability is achieved, but data transfer overhead increases
Solution Approach 1:
The patent extracts the analytics processing function from the centralized data collection path and places it directly at the network nodes where data is generated. Analytics agents are deployed at individual nodes to process data locally, extracting only the necessary analytics results rather than transferring all raw data centrally. This extraction approach maintains data availability for local processing while eliminating excessive data transfer overhead.
3Ease of operation
If centralized analytics processing is implemented, then resource utilization can be controlled centrally, but scalability is limited due to bottlenecks
Solution Approach 1:
The patent transitions from a single-dimensional centralized processing architecture to a multi-dimensional distributed architecture where analytics agents operate independently across multiple network nodes. This dimensional change from centralized to distributed processing eliminates the single-point bottleneck, enabling the system to scale horizontally by adding more nodes without compromising overall processing capacity or control efficiency.
4Loss of information
If data is processed after centralized collection, then comprehensive analysis is possible, but real-time processing capability is reduced
Solution Approach 1:
The patent implements preliminary action by deploying analytics agents at network nodes that continuously monitor and process data in real-time as it flows through the network. Rather than waiting for centralized collection, the agents perform preliminary analytics processing locally, generating insights immediately when events occur. This preliminary processing maintains analysis comprehensiveness while eliminating the time delay associated with centralized data gathering and processing.
Data Source
AI summary
A network analytics controller is established in a network. The network includes a plurality of nodes. A plurality of network analytics agents is established; each agent at a node of the network. Network analytics configuration parameters, including a network analytics scope, are received at the networks analytics controller. A task is assigned to each agent at a node determined to be within the network analytics scope, the task comprising that portion of the network analytics specified in the network analytics configuration parameters relevant to the corresponding node. The assigned task is performed at each agent assigned a task. The networks analytics controller receives the results of each performed task, and aggregates the received results.