Dynamic User Profiling for Cloud Resource Allocation
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Solution Overview
Problem
In cloud computing environments, resource scaling is typically static, leading to inefficiencies where users may either underutilize or exceed allocated resources, resulting in performance issues and suboptimal user experiences.
Innovation Solution
A dynamic resource allocation system that profiles users based on metrics such as CPU usage and past behavior, allowing for immediate upgrades or downgrades of resource allocations to match changing needs, ensuring seamless performance without impacting user experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If static resource allocation is used, then system simplicity is maintained, but resource utilization efficiency deteriorates
Solution Approach 1:
The patent implements dynamic resource allocation by continuously monitoring usage metrics (CPU, memory, storage, network) and automatically adjusting resource allocations in real-time based on actual demand, transforming the static allocation system into a dynamic one that adapts to changing workload conditions
Solution Approach 2:
The system establishes a feedback loop where usage metrics are continuously collected, analyzed, and used to trigger resource allocation adjustments. When usage thresholds are exceeded or underutilization is detected, the system automatically responds by increasing or decreasing resources, creating a closed-loop control system
2Reliability
If resource allocation is increased for all users, then performance is improved, but resource waste increases
Solution Approach 1:
The patent applies different resource allocation strategies to different user groups based on their specific usage patterns and requirements. By segmenting users into clusters with similar characteristics and applying tailored allocation policies to each cluster, the system optimizes performance for each group while avoiding unnecessary resource waste
Solution Approach 2:
The system dynamically changes resource allocation parameters (CPU, memory, storage, network bandwidth) based on monitored usage metrics and predefined thresholds. Resource allocations are adjusted incrementally as usage patterns evolve, allowing the system to respond to actual demand rather than maintaining fixed over-provisioned allocations
3Productivity
If resource allocation is decreased to save resources, then resource efficiency is improved, but user experience deteriorates
Solution Approach 1:
The system applies resource allocation adjustments selectively rather than uniformly across all users. By identifying specific user clusters that are underutilizing resources and targeting them for allocation reductions, the system achieves resource efficiency improvements while minimizing impact on users who require full resource availability
Solution Approach 2:
The system continuously monitors usage metrics and dynamically adjusts resource allocations in real-time, allowing it to respond quickly when users' needs change. This dynamic approach ensures that resource reductions do not permanently degrade user experience, as allocations can be rapidly increased if usage patterns change
4Measurement precision
If manual resource adjustment is used, then control precision is maintained, but response time increases
Solution Approach 1:
The system implements self-service automation where resource allocation adjustments are triggered automatically based on monitored usage metrics exceeding predefined thresholds. The system autonomously identifies when resource changes are needed, determines the appropriate adjustment magnitude, and executes the allocation changes without requiring manual intervention
Solution Approach 2:
The automated system continuously collects usage feedback and immediately processes it through analysis and adjustment algorithms. When usage thresholds are exceeded or underutilization is detected, the feedback loop triggers automatic resource reallocation, ensuring rapid response times while maintaining precise control through systematic decision rules
Data Source
AI summary
A method, computer program, and computer system are provided for resource allocation in a cloud computing environment. A request for resource allocation is received from a user in a cloud computing environment. A profile is determined for the user based on one or more metrics. A workload allocation is assigned to the user based on the determined profile matching one or more clusters of other users. A usage value of the assigned workload allocation to the user may be monitored. The user is immediately upgraded to a higher workload allocation based on the usage value exceeding a threshold value.