Topology for Control Plug-ins in Resource Allocation Systems
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Solution Overview
Problem
Managing resource-on-demand systems, such as grid computing and utility data centers, is labor-intensive and costly due to the need for manual resource allocation and monitoring, requiring numerous system administrators to make resource allocation decisions.
Innovation Solution
A system that receives information from control plug-ins organized in a hierarchy, determines relationships between them, and generates a topology to automate resource allocation and management, using customizable control plug-ins with parameters and control functions to adjust resource allocation based on demand and performance metrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual resource allocation and monitoring is performed using conventional management tools, then resource allocation decisions can be made, but a significant amount of time and labor is required and numerous system administrators are needed
Solution Approach 1:
The system enables automated resource allocation where the resource-on-demand system serves itself through plug-ins that automatically monitor resources, evaluate demand, and allocate resources without human intervention. This self-service mechanism eliminates the need for numerous system administrators and significantly reduces the time required for resource allocation decisions.
Solution Approach 2:
The patent replaces the mechanical manual process of resource allocation with an automated electronic system. Conventional management tools that require manual operation are substituted with plug-ins that automatically collect information, evaluate resource demand, and execute allocation decisions, transforming the manual mechanical process into an automated electronic control system.
2Ease of operation
If manual resource allocation is performed, then resource allocation decisions can be made, but it is costly and time consuming
Solution Approach 1:
The automated plug-in system performs resource allocation monitoring and decision-making autonomously. The plug-ins continuously collect information from the resource pool, evaluate demand automatically, and execute allocation without requiring system administrators to manually perform these tasks, thereby simplifying operation while dramatically reducing the time required for resource management.
3Reliability
If numerous system administrators are deployed to manage resource allocation, then resource allocation decisions can be made, but labor costs increase significantly
Solution Approach 1:
The system replaces numerous human system administrators with automated plug-ins that perform resource allocation decisions. The plug-ins continuously monitor the resource pool, evaluate demand using predefined criteria, and automatically allocate resources, maintaining reliable decision-making while eliminating the need for large numbers of human administrators and significantly reducing labor costs.
Solution Approach 2:
The plug-ins act as intermediaries between the resource pool and the management system. They automatically collect information from the resource pool, process this information according to evaluation criteria, and execute allocation decisions, serving as an automated intermediary that replaces human administrators while maintaining decision quality and reliability.
4Reliability
If conventional network management software is used for monitoring, then resource monitoring can be performed, but it is not designed to handle large resource on demand systems
Solution Approach 1:
The management system is segmented into multiple independent plug-ins, each responsible for specific resource types or functions. This modular architecture allows the system to scale by adding more plug-ins without overwhelming a single management tool, enabling the system to handle large resource-on-demand environments effectively while maintaining reliability through distributed monitoring and control.
Solution Approach 2:
The plug-in architecture provides a universal framework that can handle multiple resource types and management functions through a common mechanism. Each plug-in is designed to work with specific resources but follows a unified interface and evaluation methodology, allowing the system to scale to large environments while maintaining consistent and reliable management across diverse resource types.
Data Source
AI summary
Information from control plug-ins organized in a hierarchy is received. The control plug-ins control an allocation of resources for an application. Relationships between the control plug-ins are determined based on the received information, and a topology of the control plug-ins is generated.