Resource Cluster Chaining for Dynamic Hardware Allocation
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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 their ability to support other devices, as they rely on user-defined configurations that may not meet performance requirements.
Innovation Solution
A system that dynamically generates and evaluates different resource cluster configurations by randomly selecting combinations of hardware and software resources to identify optimal allocations that meet user-specified performance needs, using a feedback loop to improve resource utilization and reduce consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If resources are allocated based on user-defined configurations, then resource allocation simplicity is improved, but resource allocation efficiency deteriorates due to over-allocation and inability to meet performance requirements
Solution Approach 1:
The system performs self-service by automatically analyzing resource requirements and generating optimized resource cluster configurations without relying on user-defined configurations. The resource allocation engine autonomously evaluates available resources, chains resource clusters together, and allocates them to network devices based on actual performance needs, eliminating the inefficiencies of manual user-defined allocation while maintaining simplicity through automated decision-making
Solution Approach 2:
The system changes the parameter of resource configuration from static user-defined settings to dynamically generated configurations. By automatically adjusting resource cluster parameters based on real-time system state and performance requirements, the system optimizes resource allocation efficiency while maintaining ease of operation through automated parameter tuning
2Reliability
If more resources are allocated to meet user requests, then user performance requirements are satisfied, but system resource availability for other devices deteriorates
Solution Approach 1:
The system applies partial action by allocating exactly the amount of resources needed to meet user performance requirements without over-allocation. The resource allocation engine calculates precise resource needs and chains resource clusters to provide only the necessary resources, avoiding excessive allocation that would reduce availability for other devices while still satisfying user performance requirements
Solution Approach 2:
The system implements dynamic resource allocation where resource cluster configurations are generated and adjusted in real-time based on current system state and user needs. This dynamic approach allows the system to adapt resource availability across multiple devices, ensuring that resources are allocated efficiently to meet performance requirements while maintaining flexibility for other devices to access available resources
3Ease of operation
If user-defined resource configurations are used, then configuration simplicity is improved, but configuration optimization deteriorates as user-defined configurations may not meet performance requirements
Solution Approach 1:
The system performs self-service by automatically generating optimized resource cluster configurations without requiring user-defined configurations. The resource allocation engine autonomously analyzes resource requirements, evaluates available resources, and creates optimized configurations that meet performance requirements, eliminating the need for users to manually configure resources while maintaining simplicity through automated processes
Solution Approach 2:
The system implements feedback mechanisms where the resource allocation engine continuously evaluates the performance of allocated resource clusters and adjusts configurations accordingly. By monitoring whether configurations meet performance requirements and refining them through iterative optimization, the system achieves configuration optimization while maintaining ease of operation through automated feedback-driven adjustments
Data Source
AI summary
A device configured to receive requirements that identifies hardware operating characteristics and to determine a performance metric for the requirements. The device is 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 combine the first resource cluster configuration with a second resource cluster configuration, and to determine a second performance value for the combined resource cluster configuration. The device is further configured to modify the combined resource cluster configuration when the second performance value is less than the first performance value and to output the combined resource cluster configuration when the second performance value is greater than the first performance value.