Adaptive Telemetry via Cross-Domain Correlation Analysis

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Solution Overview

Problem

The overwhelming amount of telemetry data collected from various domains in computer networks poses a challenge in efficient monitoring and management, as existing systems struggle to effectively identify and reduce redundant data.

Innovation Solution

A telemetry server receives data streams from multiple sources, utilizes multi-modal deep learning to identify correlations and redundancies, and adjusts data collection by sampling and compression techniques to minimize redundant data transmission.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If telemetry data is collected from multiple domains and sources, then network monitoring coverage is improved, but data volume and redundancy increase

Engineering Contradiction:

Improvenetwork monitoring coverageVSAvoiddata volume

Core Design Contradiction:

ReliabilityVSQuantity of substance

Solution Approach 1:

The patent segments telemetry data collection by domain (application, platform, infrastructure, physical) and sources (sensors, network devices, applications), allowing targeted data gathering. Each domain can be monitored independently with appropriate sampling rates and collection intensity, preventing unnecessary data aggregation from all sources simultaneously.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system performs preliminary data processing at collection points including filtering, aggregation, and correlation analysis before data reaches the central server. Telemetry data is pre-processed to identify and remove redundant information, transforming raw data into consolidated insights that reduce overall data volume while maintaining monitoring coverage.

Inventive Principle:

Principle #10Preliminary action

2Reliability

If telemetry data is collected from multiple domains and sources, then network monitoring coverage is improved, but processing and storage requirements increase

Engineering Contradiction:

Improvenetwork monitoring coverageVSAvoidprocessing and storage requirements

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent introduces intermediate processing layers between data sources and the central server, including edge devices and regional aggregators. These intermediaries perform local data consolidation, filtering, and preliminary analysis, reducing the burden on central processing systems while maintaining comprehensive monitoring coverage.

Inventive Principle:

Principle #24Intermediary (Mediator)

3Loss of information

If redundant data is not reduced, then data completeness is maintained, but resource utilization efficiency decreases

Engineering Contradiction:

Improvedata completenessVSAvoidresource utilization efficiency

Core Design Contradiction:

Loss of informationVSLoss of energy

Solution Approach 1:

The system implements feedback mechanisms where processed telemetry data is analyzed to identify patterns and redundancies. This feedback information is used to dynamically adjust data collection strategies, sampling rates, and processing parameters, optimizing resource utilization while maintaining data completeness through adaptive rather than static collection approaches.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The patent employs parameter changes in data collection intensity based on system state, time of day, network conditions, and data redundancy analysis. Collection parameters such as sampling rates, data granularity, and transmission frequencies are dynamically adjusted to maintain information completeness while minimizing resource consumption during periods of low activity or high redundancy.

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS9998805B2Adaptive telemetry based on in-network cross domain intelligence

Publication Date: 2018.06.12 CISCO TECHNOLOGY INC

AI summary

Disclosed are systems, methods, and computer-readable storage media for adaptive telemetry based on in-network cross domain intelligence. A telemetry server can receive at least a first telemetry data stream and a second telemetry data stream. The first telemetry data stream can provide data collected from a first data source and the second telemetry data stream can provide data collected from a second data source. The telemetry server can determine correlations between the first telemetry data stream and the second telemetry data stream that indicate redundancies between data included in the first telemetry data stream and the second telemetry data stream, and then adjust, based on the correlations between the first telemetry data stream and the second telemetry data stream, data collection of the second telemetry data stream to reduce redundant data included in the first telemetry data stream and the second telemetry data stream.