Shared Classification Model for Software Discovery
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Solution Overview
Problem
Managing and mapping diverse computing resources across multiple networks is challenging due to variations in software applications and operating systems, leading to increased complexity in developing and maintaining discovery patterns, especially for remote network management platforms that need to accommodate a multitude of variants.
Innovation Solution
A shared classification model is introduced that allows multiple managed networks to contribute and utilize classifiers for software applications, enabling efficient discovery and mapping by suggesting classifications based on attributes, with feedback loops to improve accuracy over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a remote network management platform develops discovery patterns for each computing resource variant, then classification accuracy is improved, but device complexity and maintenance burden increase significantly
Solution Approach 1:
The patent merges discovery patterns from multiple managed networks into a shared repository. Instead of each network maintaining separate discovery patterns for the same computing resource variants, patterns are consolidated and shared across networks, reducing overall complexity while maintaining classification accuracy through collective intelligence.
Solution Approach 2:
The discovery pattern repository is designed as a universal system that serves multiple managed networks simultaneously. A single discovery pattern can be reused across different networks, eliminating the need for each network to develop and maintain its own set of patterns, thereby reducing device complexity while preserving classification capabilities.
2Adaptability or versatility
If each managed network independently develops discovery patterns, then adaptability to local variants is improved, but loss of time and resources for pattern development increases
Solution Approach 1:
The system performs preliminary action by having managed networks contribute their discovery patterns to the shared repository in advance. This allows other networks to immediately benefit from pre-developed patterns without needing to invest time in independent development, while the patterns remain adaptable to local variants through the shared model framework.
Solution Approach 2:
Managed networks can copy discovery patterns from the shared repository for their local use. Instead of independently developing patterns from scratch, networks can replicate proven patterns and adapt them to their specific needs, significantly reducing development time while maintaining adaptability through localized modifications.
3Ease of operation
If a small number of entities develop discovery patterns, then ease of operation is improved, but productivity in covering all computing resource variants decreases
Solution Approach 1:
The system enables self-service by allowing any managed network to contribute discovery patterns to the shared repository. This distributes the workload across multiple entities rather than relying on a small team, increasing productivity and coverage while maintaining ease of operation through automated pattern sharing and retrieval mechanisms.
Solution Approach 2:
The patent merges the capabilities of multiple entities into a unified discovery pattern repository. By combining the efforts of numerous managed networks, the system achieves comprehensive coverage of computing resource variants that would be impossible for a small team to accomplish alone, while maintaining operational simplicity through centralized management.
4Productivity
If discovery patterns are shared across managed networks, then productivity in classification is improved, but reliability may decrease due to variations in network configurations
Solution Approach 1:
The system adjusts parameters by allowing discovery patterns to be adapted to different network configurations. Managed networks can modify pattern parameters to match their specific environments while retaining the core classification logic, thereby maintaining reliability across diverse configurations while benefiting from shared productivity.
Solution Approach 2:
The patent applies local quality by allowing each managed network to customize discovery patterns according to its specific configuration characteristics. While the base patterns are shared for productivity, local modifications ensure reliability by accounting for network-specific variations, achieving both efficiency and accuracy.
Data Source
AI summary
A system may include persistent storage configured to store: a shared classification model including a plurality of classifiers based on training data from a plurality of managed networks, and a representation of a plurality of software applications executable computing devices within a particular managed network. The system may also include a discovery application configured to perform operations including obtaining attributes of a software process. The operations may also include determining, by way of the shared classification model and based on the attributes, a suggested classifier of the plurality of classifiers and determining, by way of the suggested classifier and based on the attributes, a suggested classification for the software process. The operations may further include receiving an indication that the suggested classification has been accepted, based on receiving the indication, updating the representation to indicate the suggested classification, and storing, in the persistent storage, the representation as updated.