Predictive Resource Allocation Model for Computing Applications
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Solution Overview
Problem
The allocation of hardware resources to computing applications is unpredictable, making it difficult to ensure target performance metrics are met, especially when resources become overloaded, and the impact of priority on performance degradation is unknown, leading to unpredictable performance degradation among applications.
Innovation Solution
A model is constructed to determine the performance of each computing application based on historical resource allocations and performances, allowing for predictable priority-based performance degradation by adjusting resource allocation inversely proportional to application priority during overloads, and optimizing resource distribution to balance performance across applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If hardware resources are allocated to multiple computing applications sharing the same resources, then resource utilization is improved, but performance predictability deteriorates
Solution Approach 1:
The patent changes the parameter of resource allocation from static to dynamic by introducing a model that predicts performance based on historical data. The system adjusts resource allocation parameters in real-time based on predicted performance metrics, transforming the allocation strategy from fixed to adaptive, thereby maintaining predictability while improving utilization.
Solution Approach 2:
The patent implements a feedback mechanism where the model continuously learns from historical performance data and adjusts resource allocation predictions. The system monitors actual performance, compares it with predicted performance, and uses this feedback to refine future allocations, creating a closed-loop control system that improves both utilization and predictability.
2Reliability
If hardware resources are allocated to meet target performance metrics of computing applications, then application performance is improved, but resource overload risk increases
Solution Approach 1:
The patent applies preliminary action by using the predictive model to forecast future performance and resource requirements before actual resource allocation occurs. The system proactively adjusts resource allocation based on predictions, preventing resource overload before it happens rather than reacting after the fact.
Solution Approach 2:
The patent introduces dynamics by making resource allocation flexible and adaptive rather than static. The system continuously adjusts resource allocation based on real-time conditions and predictive modeling, allowing the allocation to dynamically respond to changing workloads and prevent overload conditions.
3Object-affected harmful factors
If performance degradation is applied to computing applications when hardware resources are overloaded, then resource overload is reduced, but performance predictability deteriorates
Solution Approach 1:
The patent changes the parameter of performance degradation from arbitrary to controlled by introducing priority levels and a predictive model. The system determines which applications should experience degradation and by how much based on predicted impact and application priorities, transforming degradation from a blunt instrument to a precise control mechanism.
Solution Approach 2:
The patent applies local quality by differentiating performance degradation treatment across different applications based on their priorities and characteristics. Instead of uniform degradation, the system applies targeted degradation to specific applications where it causes minimal harm, preserving predictability through differentiated treatment.
4Reliability
If computing applications have different priorities with higher priority applications receiving more hardware resources, then application performance differentiation is improved, but resource allocation complexity increases
Solution Approach 1:
The patent simplifies the complexity by transforming the resource allocation problem into a predictive modeling problem. Instead of manually managing complex allocation rules, the system uses a model that automatically determines optimal allocations based on historical data and application priorities, reducing operational complexity while maintaining performance differentiation.
Data Source
AI summary
A model is constructed to determine performance of each computing application based on allocation of resources (including at least one hardware resource) to the computing applications. How the allocation of the resources to the computing applications affects the performance is unknown beforehand. The resources are allocated to the computing applications based at least on the model. Where the resources are overloaded as allocated to the computing applications, performance degradation of each computing application is performed based at least on priorities of the computing applications relative to one another and on the model. Performance degradation reduces usage of the resources by the computing applications so that the resources are no longer overloaded. How the priorities of the computing applications affect the performance degradation in a relative manner to one another is known and predictable beforehand.