Data Confidence Fabric Pipeline Workload Ranking Optimization
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Solution Overview
Problem
Traditional systems for configuring data management systems require manual configuration and updating as devices change, leading to inefficiencies in managing resource usage across different workloads and data sources.
Innovation Solution
A data management system that utilizes a data confidence fabric (DCF) pipeline to obtain resource usage metrics from local data systems, analyze workload rankings, and perform actions to optimize resource allocation and access control, enabling dynamic configuration and management of sub-DCFs for efficient data processing and access.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual configuration and updating is used for data management systems as devices change, then system complexity is reduced, but productivity and ease of operation deteriorate due to inefficiencies in managing resource usage across different workloads and data sources
Solution Approach 1:
The system enables automated configuration and optimization of resource usage by having the data management system automatically obtain resource usage metrics from local data systems, analyze workload rankings, and perform actions to optimize resource allocation. This self-service mechanism eliminates the need for manual configuration and updating, allowing the system to adapt automatically as devices change while improving productivity in managing resource usage across different workloads and data sources.
2Ease of operation
If automated resource usage optimization is implemented through DCF pipelines, then productivity and ease of operation improve, but device complexity increases due to the need for automated configuration and management systems
Solution Approach 1:
The data management system acts as an intermediary between local data systems and workload requirements. It automatically obtains resource usage metrics from local data systems, analyzes workload rankings, and performs optimization actions. This intermediary approach simplifies operation by automating the configuration process, while the complexity is contained within the data management system itself, which coordinates the automated configuration and optimization across multiple workloads and data sources.
3Manufacturing precision
If resource usage metrics are analyzed across multiple DCF pipelines, then manufacturing precision of resource allocation improves, but measurement precision requirements increase
Solution Approach 1:
The system implements feedback by obtaining resource usage metrics from local data systems associated with different DCF pipelines, analyzing these metrics to determine workload rankings, and using this information to perform optimization actions. This feedback loop enables precise resource allocation by continuously monitoring resource usage across multiple pipelines and adjusting allocations based on analyzed workload rankings, thereby improving manufacturing precision of resource allocation while managing measurement precision requirements through systematic analysis.
Data Source
AI summary
In general, in one aspect, the invention relates to a method for managing data, the method includes obtaining, by a data management system, a first resource usage of a local data system associated with a first data confidence fabric (DCF) pipeline, wherein the first DCF pipeline is associated with a first workload, obtaining second resource usage of the local data system associated with a second DCF pipeline, wherein the second DCF pipeline is associated with a second workload, analyzing the first resource usage and the second resource usage to obtain a workload ranking, and performing an action set based on the workload ranking.