Workload Scheduling Under Licensed Resource Capacity Limits
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Solution Overview
Problem
Traditional software licensing models are inflexible and inefficient for dynamic cloud and containerized environments, leading to overprovisioning and lack of granular licensing options, especially for specialized hardware like GPUs, due to their inability to adapt to fluctuating resource demands.
Innovation Solution
An automated and computer-implemented licensing system that tracks real-time resource usage, enforces license terms dynamically, and integrates with container orchestration platforms to schedule workloads based on actual usage, providing granular licensing and decoupling from total infrastructure capacity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional term-capacity licensing models are used, then license compliance is enforced, but resource utilization efficiency deteriorates due to overprovisioning
Solution Approach 1:
The patent transitions from static term-capacity licensing to dynamic consumption-based licensing that adapts to real-time workload demands. The system continuously monitors resource usage and adjusts license allocations dynamically, allowing organizations to pay only for actual consumption while maintaining compliance. This resolves the contradiction by making the licensing model flexible rather than fixed.
Solution Approach 2:
The patent changes the licensing parameter from fixed capacity (term-capacity model) to variable consumption (usage-based model). By measuring actual resource consumption metrics such as CPU hours, storage capacity, and network bandwidth, the system adjusts license requirements based on real usage patterns rather than predetermined allocations, improving resource utilization efficiency while ensuring compliance.
2Reliability
If total infrastructure capacity is licensed, then comprehensive coverage is provided, but cost efficiency deteriorates due to paying for unused capacity
Solution Approach 1:
The patent applies local quality by differentiating licensing across specific resource types and usage patterns rather than applying a blanket capacity license to entire infrastructure. The system monitors and licenses individual components such as compute resources, storage, and network bandwidth separately based on actual consumption, allowing organizations to pay only for the specific resources they use while maintaining comprehensive coverage.
Solution Approach 2:
The system enables self-service licensing by automatically tracking resource consumption and generating license allocations based on actual usage without requiring manual intervention. The consumption-based model allows organizations to benefit from unused capacity automatically reducing license requirements, eliminating the need to pre-purchase fixed capacity for comprehensive coverage.
3Device complexity
If static resource allocation is used, then license management is simplified, but adaptability deteriorates in response to fluctuating demands
Solution Approach 1:
The patent implements feedback mechanisms that continuously monitor resource consumption and automatically adjust license allocations in response to changing workload demands. The system tracks usage patterns, detects demand fluctuations, and dynamically modifies license requirements accordingly. This feedback loop maintains simplicity in license management while achieving high adaptability to varying resource needs.
Solution Approach 2:
The system transitions from static license allocations to dynamic licensing that automatically adapts to fluctuating demands. By continuously measuring resource consumption and adjusting license requirements in real-time, the system maintains simple management procedures while achieving flexibility in response to changing workload patterns, resolving the contradiction between simplicity and adaptability.
4Measurement precision
If granular licensing options are implemented, then resource allocation precision is improved, but system complexity increases
Solution Approach 1:
The patent implements self-service automated monitoring and tracking systems that precisely measure resource consumption across multiple dimensions without requiring complex manual management. The system automatically collects usage data, calculates consumption metrics, and generates granular license allocations based on actual usage patterns. This automation maintains high allocation precision while minimizing the operational complexity of managing granular licensing.
Data Source
AI summary
In certain implementations, a computing device includes a processor and a non-transitory computer-readable storage media storing programming for execution by the processor. The programming includes instructions to receive a request to schedule a computing workload and determine a resource type and requested resource amount for the computing workload. The programming includes instructions to obtain a total licensed capacity for the resource type, and obtain a current resource usage across existing computing workloads for the resource type. The programming includes instructions to determine whether scheduling the computing workload would cause total resource usage to exceed the total licensed capacity, and to approve, based at least on determining that the total resource usage would not exceed the total licensed capacity, the computing workload for scheduling, or to queue, based at least on determining that the total resource usage would exceed the total licensed capacity, the computing workload for later scheduling.