Dynamic Computing Resource Allocation for Multi-Tiered Applications
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for provisioning computing resources in cloud environments are inflexible and costly, as they rely on manual, static models that fail to adapt to changing user demands, leading to inefficient resource allocation and increased IT costs.
Innovation Solution
A computer-implemented method and system that dynamically determines the topology of a multi-tiered application, models computing resource settings, monitors actual usage, and adjusts resource allocation in real-time to optimize performance and cost, using a self-learning model to proactively manage resource provisioning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If manual and static models are used for provisioning computing resources, then resource allocation is simple to implement, but resource allocation efficiency deteriorates and IT costs increase
Solution Approach 1:
The system enables self-service through automated resource provisioning where the computing device autonomously determines topology, selects models, and adjusts resource allocation without manual intervention. The system monitors actual usage and dynamically provisions resources based on determined topology and modeled settings, eliminating the need for manual resource management while improving allocation efficiency.
Solution Approach 2:
The patent implements dynamics by transitioning from static resource allocation models to dynamic automated provisioning. The system continuously monitors actual usage of computing resources and adjusts allocation in real-time based on changing demands, allowing resource parameters to adapt dynamically rather than remaining fixed.
2Ease of operation
If fixed capacity templates are used for resource allocation, then resource provisioning is simplified, but adaptability to changing user demands deteriorates
Solution Approach 1:
The system replaces fixed capacity templates with dynamic resource allocation that automatically adapts to changing demands. The computing device continuously monitors actual usage and adjusts resource provisioning in real-time, enabling the system to respond flexibly to varying user demands while maintaining operational simplicity through automation.
Solution Approach 2:
The patent implements feedback mechanisms where the system monitors actual usage of computing resources and uses this information to continuously adjust resource allocation. The feedback loop enables the system to learn from actual usage patterns and adapt resource provisioning to match changing demands, improving both adaptability and operational efficiency.
3Device complexity
If generic static models are used for resource estimation, then resource allocation is easier to manage, but resource allocation accuracy deteriorates
Solution Approach 1:
The system transitions from generic static models to dynamic customized models that adapt to specific application topologies and usage patterns. The computing device determines the application topology, selects appropriate models based on topology characteristics, and continuously refines resource estimates based on actual usage data, improving accuracy without significantly increasing management complexity through automation.
Solution Approach 2:
The patent applies local quality by customizing resource estimation models according to specific application topology characteristics and usage patterns rather than using uniform generic models. Each application receives tailored resource allocation based on its specific requirements and actual performance data, improving estimation accuracy for diverse workloads.
4Device complexity
If initially allocated computing resources are not monitored and tuned, then resource management is simpler, but resource utilization efficiency deteriorates
Solution Approach 1:
The system implements continuous monitoring and feedback loops where actual usage of computing resources is tracked and fed back to the resource allocation mechanism. This enables automatic tuning of resource allocation based on real-world performance data, improving utilization efficiency while managing complexity through automated control systems.
Solution Approach 2:
The patent enables self-service resource optimization where the system autonomously monitors its own resource usage and automatically adjusts allocation without external intervention. The computing device independently determines when and how to reallocate resources based on monitored actual usage, improving efficiency while keeping management simple through self-management.
Data Source
AI summary
Systems and methods for allocating computing resources for a multi-tiered application are disclosed. A computer-implemented method includes: determining, by a computing device, a topology of a multi-tiered application; determining, by the computing device, a modeled setting of a computing resource for the multi-tiered application based on the determined topology; determining, by the computing device, an actual usage of the computing resource by the multi-tiered application; and adjusting, by the computing device, an allocation of the computing resource to the multi-tiered application based on the actual usage and the modeled setting.