Dynamic Cloud Resource Allocation System
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Solution Overview
Problem
In cloud computing environments, existing resource allocation methods lead to inefficient utilization of resources, resulting in wasted capacity, high costs, and performance issues due to static and uniform allocation practices, which fail to adapt to varying consumption patterns of resource consumers.
Innovation Solution
A dynamic resource allocation method that sets individual maximum and minimum consumption thresholds for each resource consumer, allowing for reallocation of unutilized resources to those in greater need, based on current and historical consumption patterns, thereby optimizing resource distribution and utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If resources are statically and uniformly partitioned among resource consumers, then each consumer has guaranteed resource availability, but resource utilization efficiency deteriorates
Solution Approach 1:
The patent implements dynamic resource allocation by continuously monitoring resource consumption patterns and adjusting allocations in real-time. The system transitions from static uniform partitioning to dynamic adaptive allocation, where resource caps are adjusted based on actual usage patterns, ensuring both availability and efficiency.
Solution Approach 2:
The system changes the parameter of resource allocation from fixed uniform values to variable values based on consumption patterns. By monitoring and analyzing resource usage metrics, the system adjusts allocation parameters dynamically to optimize utilization while maintaining service level agreements.
2Loss of energy
If resource caps are set too low to prevent waste, then resource waste is reduced, but service level agreements are violated
Solution Approach 1:
The patent implements feedback mechanisms by continuously monitoring resource consumption and comparing it against allocated caps. When consumption patterns indicate upcoming violations, the system proactively adjusts caps to prevent SLO breaches while minimizing waste through data-driven decision-making.
Solution Approach 2:
The system performs preliminary actions by analyzing historical consumption patterns to predict future resource needs. Resource caps are adjusted in advance based on predicted usage, preventing both waste and SLO violations before they occur.
3Reliability
If resource caps are set too high to ensure sufficient resources, then service level agreements are met, but resource waste increases
Solution Approach 1:
Through continuous monitoring of actual resource consumption versus allocated caps, the system receives feedback that enables precise optimization. Caps are adjusted based on real usage data, eliminating the need for excessive buffering while ensuring SLO compliance.
Solution Approach 2:
The system dynamically changes resource cap parameters from static high-values to optimized values based on actual consumption patterns, reducing waste while maintaining reliability.
4Ease of operation
If resources are equally allocated to all resource consumers, then fairness is maintained, but adaptability to varying consumption patterns deteriorates
Solution Approach 1:
The patent applies local quality by customizing resource allocations to match individual consumer characteristics and usage patterns. Each resource consumer receives tailored allocations based on their specific needs and historical behavior, rather than uniform treatment.
Solution Approach 2:
The system transitions from static equal allocation to dynamic adaptive allocation that responds to varying consumption patterns while maintaining fairness through transparent, data-driven decision-making.
Data Source
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AI summary
The present invention concerns a method for allocating at least one resource to a plurality of resource consumers in a cloud computing environment. The method comprises the steps of defining a maximum resource consumption for each of the plurality of resource consumers; measuring a current resource consumption of each of the plurality of resource consumers;calculating based on the maximum resource consumptions and the current resource consumptions an unutilized amount of the at least one resource; and allocating at least a portion of the unutilized amount of the at least one resource to the plurality of resource consumers.