Dynamic Policy Blades for Network Data Collection
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Solution Overview
Problem
Existing network management systems lack adaptability and self-management capabilities, leading to difficulties in synchronizing accounting and performance data, and they are limited in handling dynamic changes and incomplete data, which hampers effective network diagnosis and optimization.
Innovation Solution
A method and apparatus for managing performance and accounting data using dynamically installed policies and collector-blades, with a policy-kernel managing the system, enabling adaptable polling, aggregation, and filtering, and allowing for the integration of new features and policies to optimize data collection and processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If hard-coded software structure is used for data collection, then system stability is maintained, but adaptability to new features and dynamic changes is lost
Solution Approach 1:
The patent implements dynamic policy blades that can be loaded and executed at runtime, allowing the system to adapt its data collection behavior without recompilation. The policy kernel dynamically loads policy blades from files, enabling the system to change its monitoring behavior in response to varying network conditions and requirements.
Solution Approach 2:
The patent segments the data collection system into modular components: collector blades for different data types, policy blades for control logic, and a policy kernel for coordination. This segmentation allows independent development, testing, and deployment of individual components while maintaining overall system stability.
2Reliability
If separate accounting and performance data collection is implemented, then data specialization is achieved, but synchronization between data types becomes difficult
Solution Approach 1:
The patent merges accounting and performance data collection into a unified framework managed by the policy kernel. Both collector types operate under common policy control and feed into a single data processing pipeline, ensuring synchronized and consistent data availability while maintaining specialized collection capabilities.
3Productivity
If simple aggregation functions are used for data processing, then processing speed is maintained, but diagnostic capability for incomplete data is limited
Solution Approach 1:
The patent changes the processing parameters dynamically based on data completeness and quality metrics. When data is incomplete or suspicious, the system adjusts aggregation parameters to perform more rigorous validation and analysis, while maintaining high processing speed for complete, high-quality data streams.
Solution Approach 2:
The system implements feedback mechanisms where processing results inform subsequent collection and processing parameters. Diagnostic outcomes feed back into policy adjustments, allowing the system to learn from incomplete data patterns and improve future diagnostic precision without sacrificing overall processing throughput.
4Extent of automation
If external decisions are required to modify polling mechanisms, then system control is maintained, but self-adaptation capability is lost
Solution Approach 1:
The policy kernel provides self-service capabilities by automatically loading, validating, and executing policy blades based on system state and incoming data. The system monitors its own performance and automatically adjusts collection parameters through embedded policies without requiring external intervention, while maintaining audit trails for accountability.
Data Source
AI summary
A method of managing performance data and accounting data that are generated in a computer network comprises collecting performance data from the network using one or more collector blades that are installed into a data collection manager at a time when the data collection manager is executed; determining whether the performance data satisfies one or more thresholding rules, and if so, generating and sending one or more performance messages; filtering the performance data using one or more filters; aggregating the performance data; and correlating the performance data with other data received from one or more network management subsystems.