Dynamic Resource Allocation via Client Classification
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Solution Overview
Problem
Infrastructure providers face suboptimal resource allocation due to varying client needs and changes in resource availability over time, leading to inefficient use of computing resources.
Innovation Solution
A method for client classification-based dynamic allocation of computing infrastructure resources, using infrastructure usage data to assign classification values and generate recommendation mappings for resource allocation, allowing for iterative adjustments based on metrics such as performance, cost, and client satisfaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If initial resource allocation is made based on available resources and expected usage, then resources can be quickly assigned to clients, but the allocation becomes suboptimal over time as actual usage varies and resource availability changes
Solution Approach 1:
The patent implements dynamic resource allocation by continuously monitoring client resource usage metrics and automatically reassigning resources based on actual usage patterns. The system transitions from static initial allocation to dynamic adjustment, where resource assignments are updated in response to changing conditions, ensuring optimal allocation efficiency while adapting to varying client needs over time
Solution Approach 2:
The system employs feedback mechanisms by monitoring actual resource usage metrics from clients and using this information to adjust future resource allocations. The feedback loop collects data on actual versus expected usage, identifies deviations, and triggers reallocation actions to optimize resource distribution, thereby resolving the contradiction between initial quick allocation and ongoing adaptability
2Device complexity
If resources are statically allocated based on initial expectations, then allocation decisions are simple and quick to make, but they become suboptimal when actual usage differs from expectations
Solution Approach 1:
The system enables self-service resource optimization by automatically monitoring usage patterns and performing reallocation without requiring complex manual intervention. The automated monitoring and adjustment mechanisms handle the complexity internally, keeping allocation decision complexity low for users while maintaining high resource utilization efficiency through continuous optimization
3Productivity
If resource allocation is frequently adjusted to match actual usage, then resource allocation efficiency is optimized, but client disruptions may increase
Solution Approach 1:
The system performs preliminary monitoring and analysis of resource usage patterns before executing reallocation actions. By提前 identifying optimization opportunities and preparing allocation changes in advance, the system can implement adjustments with minimal disruption to client services, thereby maintaining both high resource allocation efficiency and service continuity
Solution Approach 2:
The system implements cushioning mechanisms by maintaining buffer resources and preparing contingency allocation plans before changes are needed. This allows the system to optimize resource allocation while having pre-prepared fallback options that prevent service disruptions, thus resolving the contradiction between efficiency optimization and service reliability
Data Source
AI summary
Methods and apparatus for classification-based dynamic allocation of computing resources are described. A method comprises determining usage data sources corresponding to one or more clients of a computing infrastructure, and assigning values to client classification categories associated with a particular client based on metrics obtained from the particular client's usage data sources. The method includes generating a recommendation mapping between values of the client classification categories, and one or more resources of the infrastructure, based at least in part on resource classification information. The method further includes allocating at least a portion of the one or more resources to the particular client based at least in part on the recommendation mapping.