Dynamic Resource Clustering for Network Device Allocation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing computer systems lack the ability to efficiently allocate hardware and software resources to network devices, often over-allocating resources which limits system support for other devices and fails to meet performance requirements.
Innovation Solution
A dynamic resource clustering architecture that generates and evaluates different resource configurations by randomly selecting combinations of hardware and software resources to identify optimal allocations that meet user-specified performance needs, using feedback loops to improve resource utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing computer systems allocate resources based on user specifications, then users can request needed resources, but the system lacks the ability to analyze whether the configuration meets performance requirements and may over-allocate resources
Solution Approach 1:
The system implements a feedback mechanism where generated resource configurations are evaluated against performance requirements and user feedback. The system analyzes whether allocated resources meet performance needs and adjusts allocations accordingly, creating a closed-loop system that continuously improves resource allocation efficiency based on actual performance data and user requirements.
Solution Approach 2:
The system dynamically changes resource allocation parameters by generating multiple different resource configurations and selecting optimal ones. Instead of static user-specified allocations, the system varies hardware resources, software resources, and their combinations to find configurations that optimize performance while meeting user requirements, thereby improving both adaptability and allocation efficiency.
2Reliability
If the system allocates more resources to meet performance requirements, then network devices can achieve better performance, but fewer resources are available for other network devices
Solution Approach 1:
The system applies partial action by allocating only the necessary resources required to meet performance requirements rather than over-allocating. Through generated and evaluated configurations, the system identifies minimal sufficient resource sets that satisfy network device performance needs, preventing resource waste and ensuring availability for other devices while maintaining reliable performance.
Solution Approach 2:
The system performs preliminary resource configuration generation and evaluation before actual resource allocation. By pre-generating multiple resource configurations and assessing their effectiveness against performance requirements, the system determines optimal allocations in advance, ensuring that resources are allocated efficiently and adequately without excess, thereby maintaining resource availability for multiple devices.
3Ease of manufacture
If the system uses fixed resource allocation methods, then implementation is simple, but the system cannot discover new resource combinations that may provide improved performance
Solution Approach 1:
The system transitions from fixed static resource allocation to dynamic configuration generation. The system continuously generates new resource combinations, evaluates their performance, and adapts allocations based on results. This dynamic approach enables discovery of optimized resource configurations that improve performance while maintaining manageable implementation through automated generation and evaluation processes.
Solution Approach 2:
The system performs self-service by automatically generating, evaluating, and selecting optimal resource configurations without requiring manual intervention. The system analyzes performance requirements, generates appropriate resource combinations, assesses their effectiveness, and implements allocations autonomously, thereby achieving performance optimization while keeping implementation simple through self-automated operation.
Data Source
AI summary
A device configured to receive requirements that identifies hardware operating characteristics and to determine a performance metric based on the requirements. The device is further configured to generate a set of resource cluster configurations that each identify a set of hardware resources and a set of algorithms. The device is further configured to identify a first resource cluster configuration having a first performance value with the highest performance value from among the set of resource cluster configurations. The device is further configured to modify the first resource cluster configuration and to determine a second performance value for the modified resource cluster configuration. The device is further configured to modify the modified resource cluster configuration when the second performance value is less than the first performance value and to output the modified resource cluster configuration when the second performance value is greater than the first performance value.