KPI Metrics Manager for Edge Data Processing
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Solution Overview
Problem
The increasing data traffic in 5G networks, driven by IoT and real-time applications, poses challenges to traditional data processing platforms, leading to inefficient and resource-intensive KPI management, with no centralized system for KPI development, cataloging, or metadata management, resulting in lengthy development cycles and inability to share or reuse algorithms.
Innovation Solution
A centralized system with a network edge data collector and KPI engine, managed by a KPI metrics manager, which creates, modifies, and versions KPI algorithms, provides a REST API for access, and uses machine learning to optimize data processing, enabling efficient scaling and deployment of KPIs across the network.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If custom KPI algorithms are created for each microservice or application, then the specific monitoring needs of each system can be met, but the development cycle becomes lengthy and resources are consumed without the ability to share or reuse the algorithms
Solution Approach 1:
The patent creates a universal KPI algorithm marketplace that allows algorithms to be developed once and reused across multiple microservices and applications. The platform enables KPI algorithms to serve multiple purposes and systems simultaneously, eliminating redundant development while maintaining the ability to customize algorithms for specific monitoring needs through selective selection and configuration from the marketplace.
2Adaptability or versatility
If KPI and source metadata definitions are passed by notes and files specific to each microservice or application, then each system can maintain its own definitions, but there is no centralized user interface for KPI development, cataloging, or source metadata management
Solution Approach 1:
The patent merges分散的KPI and metadata management into a centralized platform that provides a unified user interface for KPI development, cataloging, and management. The system combines the benefits of centralization (standardization, reusability) with system independence by allowing each microservice to select and use KPI definitions from the centralized catalog, maintaining operational autonomy while simplifying overall management.
3Extent of automation
If large amounts of data are moved or shared from edge devices to central locations, then centralized processing can be achieved, but an extra traffic load is introduced to the network and unacceptable delays occur for time-sensitive microservices/applications
Solution Approach 1:
The patent segments the KPI processing architecture into edge components and central components. Edge KPI engines perform local data processing and KPI calculation at the network edge, while the centralized KPI algorithm marketplace provides algorithm management and distribution. This segmentation allows time-sensitive processing to occur locally at edge devices, eliminating network delays, while still enabling centralized algorithm updates and management.
Data Source
AI summary
A system includes a first network edge data collector, a first network edge key performance indicator (KPI) engine configured to operate on first data collected by the first network edge data collector, a KPI metrics manager in communication with the first network edge KPI engine, the KPI metrics manager controlling a KPI metric catalog and wherein the first network edge KPI engine determines first edge KPI metric using a metric algorithm from the KPI metric catalog on the first data.