Cognitive System Predicting QoS in Data Backplane Services

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Solution Overview

Problem

Distributed computing systems face challenges in managing data movement and replication while meeting quality-of-service (QoS) requirements, particularly in complex applications like financial services fraud detection, where data integrity and consistency are critical and often compromised due to the segmented nature of microservices.

Innovation Solution

A method is introduced that uses a cognitive system trained with historical operational data to predict potential QoS issues, which then invokes data backplane services to optimize data movement and replication, ensuring QoS criteria are met by prioritizing services based on likelihood values and using scalable infrastructure for elastic scaling.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If data is distributed across microservices, then system modularity and resilience are improved, but data integrity and consistency deteriorate

Engineering Contradiction:

Improvesystem modularityVSAvoiddata integrity

Core Design Contradiction:

Adaptability or versatilityVSReliability

Solution Approach 1:

The system continuously monitors data quality metrics across microservices and uses this feedback to dynamically adjust data movement operations, ensuring consistency is maintained despite distributed architecture

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The system performs preliminary assessments of data quality and operational outcomes before executing data movement operations, preventing integrity issues before they occur

Inventive Principle:

Principle #10Preliminary action

2Reliability

If data movement operations are increased, then data consistency is improved, but system performance and response time deteriorate

Engineering Contradiction:

Improvedata consistencyVSAvoidsystem performance

Core Design Contradiction:

ReliabilityVSProductivity

Solution Approach 1:

The system performs only the necessary data movement operations required to maintain consistency, avoiding excessive operations that would degrade performance

Inventive Principle:

Principle #16Partial or excessive action

Solution Approach 2:

The system dynamically adjusts data movement parameters such as frequency, volume, and timing based on current operational conditions to balance consistency requirements with performance constraints

Inventive Principle:

Principle #35Parameter changes

3Device complexity

If manual monitoring and management is used, then system complexity is reduced, but labor costs and response time increase

Engineering Contradiction:

Improvesystem complexityVSAvoidresponse time

Core Design Contradiction:

Device complexityVSLoss of time

Solution Approach 1:

The system automatically monitors its own data quality metrics and performs self-optimization of data movement operations without requiring manual intervention

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system uses automated feedback loops to continuously monitor operational outcomes and adjust data management operations in real-time, eliminating manual monitoring delays

Inventive Principle:

Principle #23Feedback

4Productivity

If data is replicated across multiple locations, then data availability is improved, but data integrity and consistency deteriorate

Engineering Contradiction:

Improvedata availabilityVSAvoiddata consistency

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The system monitors data quality across replicated locations and uses feedback to coordinate updates and maintain consistency while preserving availability

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The system dynamically adjusts replication strategies based on current operational conditions, data types, and consistency requirements

Inventive Principle:

Principle #15Dynamics

Data Source

PatentUS11277317B2Machine learning to predict quality-of-service needs in an operational data management system

Publication Date: 2022.03.15 INTERNATIONAL BUSINESS MACHINE CORPORATION

AI summary

Operational data in a distributed processing system is managed by monitoring a workload of the system to establish a current assessment of operational data movement between data sources and data targets, receiving historical information on previous data movement including previous instances of movement resulting in a compromise of one or more quality-of-service criteria, determining from the current assessment and historical information that upcoming operational data actions will not meet a particular quality-of-service criterion, and responsively applying a data management optimization infrastructure (data backplane services) adapted to advance the particular quality-of-service criterion according to definitions for the data sources and data targets. The operational outcome is predicted using a cognitive system trained with historical information including historical operational factors correlated with historical operational outcomes relative to the quality-of-service criteria.