Unified Data Collection Workflow for Heterogeneous Networks
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Solution Overview
Problem
Managing data collection from heterogeneous networks that include SNMP-compliant, non-SNMP compliant, and hybrid devices is cumbersome and inefficient, requiring disparate engines and software applications, leading to cross-team collaboration challenges and delayed customer requests.
Innovation Solution
A unified data collection workflow architecture that uses workflow scripts and engines to abstract data collection tasks, leveraging plug-ins for various technologies and protocols, allowing for modularization and flexibility in data collection across different devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If SNMP protocol is used for data collection, then data collection is standardized and simplified, but it cannot collect data from non-SNMP compliant devices or all components of hybrid devices
Solution Approach 1:
The patent implements a universal data collection architecture that supports multiple protocols (SNMP, REST, Telemetry, gRPC) within a single system. The normalized data model and protocol adapter layer enable the system to collect data from diverse device types using different protocols, making the data collection process adaptable to various device implementations while maintaining operational simplicity.
Solution Approach 2:
The patent introduces a protocol adapter layer and normalized data model as intermediary components between the data collection engine and diverse device protocols. This mediator layer translates protocol-specific data into a unified format, allowing the system to maintain simple standardized operations while accommodating multiple device protocols through the intermediary translation layer.
2Adaptability or versatility
If multiple disparate engines and software applications are used to manage different device types, then each device type can be managed with specialized tools, but system complexity increases and cross-team collaboration becomes difficult
Solution Approach 1:
The patent merges multiple disparate data collection engines into a single unified data collection system. By combining support for SNMP, REST, Telemetry, and gRPC protocols within one architecture using a common normalized data model and engine, the system reduces architectural complexity while maintaining the ability to handle diverse device types that previously required separate specialized tools.
Solution Approach 2:
The unified data collection engine is designed to perform multiple functions by supporting various protocols and device types through a single system. This multi-functional approach eliminates the need for multiple separate engines and software applications, simplifying the overall system architecture while preserving comprehensive device type coverage.
3Reliability
If traditional multi-team approach is used for data collection management, then specialized teams can maintain their respective protocols, but updates and customer requests experience delays due to cross-team collaboration requirements
Solution Approach 1:
The patent consolidates protocol maintenance responsibilities into a single unified data collection engine that handles multiple protocols simultaneously. This merging eliminates the need for cross-team collaboration for protocol updates, as a single team can maintain and update the unified engine, thereby improving productivity and update speed while maintaining reliable protocol support through the engine's standardized architecture.
Solution Approach 2:
The unified data collection engine provides a universal platform for maintaining multiple protocols within a single codebase and team structure. This multi-functional engine allows a single team to reliably maintain various protocols while avoiding the delays associated with cross-team coordination, thus improving both reliability and productivity simultaneously.
Data Source
AI summary
A data collection workflow architecture can streamline data collection from a heterogeneous network and leverage plug-ins of various technologies for data collection from the heterogeneous network. The data collection workflow architecture uses a workflow definition which is an abstracted series of data collection tasks. The workflow definition expresses the data collection tasks at a level abstracted away from the underlying implementation to increase the ease and efficiency of managing and maintaining data collections on a heterogeneous network. A workflow engine transforms each of the higher level expressions of data collections tasks into task definitions that can be passed to a plug-in engine. The task definition can be considered the abstracted process level expression of a data collection task with variable values injected into it. The plug-in engine hooks a task definition into an implementation of the task definition and executes or interprets the task definition implementation with the variable values.