Policy-Based Cloud Resource Allocation for QoS
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Solution Overview
Problem
Existing cloud computing systems fail to consider priorities and service requirements when allocating cloud and network resources, leading to suboptimal quality of service and inefficient resource utilization.
Innovation Solution
A policy-based approach is implemented to dynamically allocate cloud and network resources, taking into account user priorities and service requirements, using a cloud management system that integrates components such as monitoring, prediction, simulation, and orchestration to ensure optimal resource allocation and quality of service.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If cloud resources are allocated based on load balancing information and statistical distribution, then resource allocation is automated and scalable, but quality of service requirements and user priorities are not considered
Solution Approach 1:
The system dynamically adjusts resource allocation by transitioning from static load balancing to adaptive policy-based allocation. The cloud management system continuously monitors service requirements and user priorities, then dynamically reallocates resources to match changing conditions while maintaining automated operation.
Solution Approach 2:
The invention changes the allocation parameters from simple load balancing metrics to multi-dimensional parameters including user priorities, service requirements, and quality of service thresholds. This parameter transformation enables the automated system to consider qualitative factors alongside quantitative load metrics.
2Speed
If cloud resources are allocated using statistical distribution methods, then allocation speed is high, but resource utilization efficiency deteriorates due to ignoring service requirements
Solution Approach 1:
The system performs preliminary classification of service requirements and user priorities before resource allocation occurs. By pre-establishing policy rules and service level agreements, the system maintains fast allocation speeds while ensuring resources are directed to appropriate services based on their specific requirements.
Solution Approach 2:
The invention implements feedback mechanisms where resource allocation decisions are continuously evaluated against actual service performance and quality of service metrics. This feedback loop enables the system to adjust allocation patterns to improve overall resource utilization efficiency while maintaining operational speed.
Data Source
AI summary
A device may receive a request for a particular service from a user device. The device may determine a priority class associated with the service. The device may determine, based on the priority class, quality of service requirements associated with providing the service. The device may determine, based on the quality of service requirements, one or more cloud resources for providing the service to a user of the user device. The device may allocate the one or more cloud resources to provide the service to the user.