Automated Data Plane Generation Using CSP Solver
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Solution Overview
Problem
Current cloud-based data access and governance systems lack an automated, end-to-end mechanism for orchestrating secure and optimized data utilization across multi-cloud environments, leading to inefficient resource consumption and manual processes for data management.
Innovation Solution
A computer-implemented method using a Constraint Satisfaction Problem (CSP) solver to generate and deploy optimized data planes, comprising software modules that connect workloads to governed datasets based on available IT infrastructure, data governance policies, and IT configuration policies, enabling on-demand automated generation of customized data planes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual processes are used for data management and orchestration, then flexibility and control are maintained, but resource consumption increases and efficiency decreases
Solution Approach 1:
The system implements self-service through automated data plane generation and orchestration. The CSP solver automatically generates optimized data planes without manual intervention, and the system self-manages the deployment and configuration of software modules across infrastructure components, eliminating the need for manual orchestration while improving efficiency and reducing resource consumption.
Solution Approach 2:
The system changes parameters by dynamically optimizing data plane configurations based on real-time conditions. The CSP solver adjusts various parameters including software module selection, infrastructure resource allocation, and data flow routing to achieve optimal performance while minimizing computing resource consumption.
2Productivity
If automated data plane generation is implemented, then resource optimization and efficiency improve, but system complexity increases
Solution Approach 1:
The system introduces intermediary components including the CSP solver as a mediator between data requests and infrastructure resources. This intermediary automatically handles the complex task of generating optimized data planes, managing software module deployments, and coordinating data flows, thereby simplifying the overall system architecture while achieving resource optimization.
Solution Approach 2:
The system segments the data plane generation process into distinct modular components: the CSP solver for optimization, software modules for specific functions, and infrastructure components for execution. This segmentation allows each component to be independently managed and optimized, reducing overall system complexity while improving efficiency.
3Use of energy by moving object
If optimized data planes are generated using CSP solver, then computing resource consumption is reduced, but the time required for data plane generation may increase
Solution Approach 1:
The system performs preliminary action by pre-generating and caching optimized data plane configurations using the CSP solver. When data requests are made, pre-computed optimization results are quickly applied, reducing the time required for actual data plane generation while maintaining the resource consumption benefits of optimization.
Data Source
AI summary
System, method and computer program products for generating optimized and customized data planes are provided. In embodiments, a method includes: receiving a data request from a client device in a network, the data request including information regarding one or more governed datasets required by a workload; identifying attributes of available information technology (IT) infrastructure in the network; generating a blueprint of a data plane for the one or more governed datasets using a Constraint Satisfaction Problem (CSP) solver, the blueprint including required software modules for the workload, a subset of the available IT infrastructure to execute the required software modules, and instructions for a flow of data between the required software modules based on predetermined IT configuration policies; and deploying the data plane in the network based on the blueprint, thereby connecting the workload to the one or more governed datasets by the required software modules.