Logical KPI Network for Cloud Anomaly Detection

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Solution Overview

Problem

Large cloud computing networks face challenges in managing and detecting computer performance anomalies due to the massive amount of Key Performance Indicators (KPIs) generated, which are difficult to handle manually and require scalable automated tools that effectively utilize logical networking.

Innovation Solution

A logical KPI network is implemented, comprising database, application server, and web server nodes and edges that propagate KPI data to a KPI server system, processing logical data path information to detect computer performance anomalies, enabling scalable detection across large deployments.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If manual monitoring of computer KPIs is used, then detailed performance analysis is possible, but scalability is poor and cannot handle massive amounts of KPI data

Engineering Contradiction:

ImproveKPI processing capacityVSAvoidManual monitoring requirement

Core Design Contradiction:

ProductivityVSExtent of automation

Solution Approach 1:

The system enables self-service through automated anomaly detection where the logical KPI network automatically monitors, processes, and detects performance anomalies without requiring manual intervention. The network self-diagnoses and reports issues, freeing technicians from manual monitoring of massive KPI datasets.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces manual mechanical monitoring with an automated computational system. Human analysts are substituted by a logical KPI network that uses computer processing to monitor, aggregate, and detect anomalies in KPI data, transforming a manual process into an automated digital system.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Productivity

If automated tools are developed to process KPIs, then scalability is improved, but effectiveness in detecting anomalies is insufficient

Engineering Contradiction:

ImproveKPI processing capacityVSAvoidAnomaly detection effectiveness

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The system segments KPI data into different types (database KPIs, application server KPIs, web server KPIs) and processes them through specialized logical nodes. This segmentation allows targeted anomaly detection for different service layers, improving both processing capacity and detection effectiveness by analyzing each KPI type through appropriate logical filters and patterns.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The logical KPI network implements feedback mechanisms where processed KPI data and detected anomalies feed back into the system for continuous improvement. The network learns from processed data to refine its anomaly detection algorithms, improving reliability over time while maintaining high processing capacity through automated feedback loops.

Inventive Principle:

Principle #23Feedback

3Productivity

If logical networking is used for data mining, then data processing capability is improved, but effectiveness for KPI anomaly detection is insufficient

Engineering Contradiction:

ImproveData processing capabilityVSAvoidKPI anomaly detection difficulty

Core Design Contradiction:

ProductivityVSDifficulty of detecting and measuring

Solution Approach 1:

The logical KPI network is designed as a universal system that handles multiple functions: data collection, data aggregation, anomaly detection, and alert generation. This multi-functional approach consolidates what would otherwise require separate systems into a single unified network, improving processing capability while simplifying KPI anomaly detection through integrated logic.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

The system changes parameters by transforming raw KPI data into logical representations and changing the state of data from individual metrics to aggregated patterns. By changing the parameter representation and aggregation level, the system makes anomaly detection more effective while maintaining high processing capability through optimized data transformations.

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS10999122B2Identification of computer performance anomalies with a logical key performance indicator network

Publication Date: 2021.05.04 ORACLE INT CORP

AI summary

In an exemplary embodiment, a computer system hosts a logical Key Performance Indicator (KPI) network to detect computer performance anomalies. Databases execute database KPI nodes, database edges, and database instance nodes of the logical KPI network to propagate database KPI data to a KPI server system. Application servers execute application server KPI nodes, application server edges, and application server instance nodes of the logical KPI network to propagate application server KPI data to the KPI server system. Web servers execute web server KPI nodes, web server edges, and web server instance nodes of the logical KPI network to propagate web server KPI data to the KPI server system. This KPI data indicates logical data path information for the propagated KPI data (instead of KPI values). The KPI server system processes the logical data path information to indicate the computer performance anomalies.