Programmable License Management for Multi-Tenant Compliance Without Downtime
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Solution Overview
Problem
Current license management and enforcement systems for multi-tenant systems, such as SaaS, face challenges in supporting new licenses without causing downtime, consuming computing resources, and failing to adequately manage licenses, leading to delays and inefficiencies.
Innovation Solution
A programmable model-driven license system that processes license data to generate combined entitlements, maps entitlements to capabilities, and uses a machine learning model to predict future usage, enabling efficient license management and enforcement without requiring updates to microservices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional license management systems are used to support new licenses, then license compliance can be enforced, but system downtime occurs and computing resources are consumed
Solution Approach 1:
The system performs preliminary actions by pre-compiling license rules into executable policy representations and pre-establishing entitlement-to-capability mappings before license changes are needed. This allows the license management system to respond to new licenses immediately without requiring runtime compilation or microservice updates, thereby preventing downtime while maintaining compliance enforcement.
Solution Approach 2:
The patent introduces an intermediary license management system that acts as a mediator between license data and microservices. This intermediary layer translates license entitlements into capability mappings and enforces compliance without requiring direct modifications to microservices, thus avoiding system downtime and reducing computing resource consumption during license updates.
2Reliability
If traditional license management systems are updated to support new licenses, then license compliance can be maintained, but computing resources are consumed and delays occur
Solution Approach 1:
The system replaces traditional mechanical update processes with a programmable model-driven approach. Instead of manually updating microservices and license management code to support new licenses, the system automatically translates license entitlements into executable policy representations and capability mappings, eliminating the need for computational resources to be consumed on manual updates and reducing delays.
Solution Approach 2:
The patent changes the parameter representation of license management by transforming license entitlements into structured policy representations with specific parameters (entitlement-to-capability mappings). This parameter transformation enables automated license compliance enforcement without requiring system updates, thereby maintaining compliance while improving management efficiency and reducing delays.
3Productivity
If license management systems process license data dynamically, then resource utilization is optimized, but system complexity increases
Solution Approach 1:
The system segments license management into distinct modular components: license data reception, entitlement identification, policy representation generation, entitlement-to-capability mapping, and compliance enforcement. This segmentation allows dynamic processing of license data to optimize resource utilization while managing complexity through clear separation of concerns, where each module handles a specific aspect of license management independently.
Data Source
AI summary
A device may receive license data identifying device licenses and organization licenses associated with an organization of users of a multi-tenant system, and may identify, in the license data, entitlements for licenses associated with the organization. The device may combine the entitlements to generate combined entitlements, and may determine an entitlement count of the combined entitlements. The device may add quantities of new entitlements to the entitlement count, and may identify, in the license data, roles of the users and capabilities associated with each of the roles. The device may map the entitlements and the capabilities to generate a mapping, and may authorize a particular user based on the mapping. The device may process usage of the entitlements, with a machine learning model, to predict future usage of the entitlements, and may determine entitlement recommendations based on the future usage. The device may provide the entitlement recommendations for display.