Modular Computing System Automated Configuration
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Solution Overview
Problem
Existing integrated computing and storage systems face challenges in automating the integration and management of modular components, leading to high operational costs and inefficient resource utilization due to the need for manual configuration and customization of network adapters, protocols, and resource allocation.
Innovation Solution
A modular computing system with a common network interface, initialization, monitor, and management modules that automatically configure and manage components to optimize performance by dynamically adjusting resource allocation and network structure based on predefined policies, allowing for the addition or removal of components without user intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual configuration of network adapters, protocols, and resource allocation is performed, then system integration is achieved, but operational costs and time consumption increase significantly
Solution Approach 1:
The system performs preliminary configuration actions automatically when modular components are added. The initialization module pre-configures network adapters, protocols, and resource allocation before the component is fully integrated, eliminating the need for manual configuration time and ensuring immediate interoperability.
Solution Approach 2:
Modular components perform self-configuration through the initialization module without requiring manual intervention. The system automatically detects new components, applies appropriate configurations based on component type, and integrates them into the network, making the system self-sufficient in its integration processes.
2Reliability
If components are designed for surge requirements with excess capability, then system reliability during peak demand is ensured, but operational costs increase due to maintaining unused capacity
Solution Approach 1:
The system dynamically adjusts its capacity by automatically adding or removing modular components based on real-time demand monitoring. During surge periods, components are rapidly added to meet demand; during normal periods, excess components are removed or placed in standby mode, optimizing the balance between reliability and operational cost.
Solution Approach 2:
The system changes operational parameters such as active component count, resource allocation, and network configuration based on demand conditions. This allows the system to transition between high-capability mode during surges and optimized cost-mode during normal operation, adapting to changing requirements.
3Adaptability or versatility
If modular components are added to expand capability, then system functionality is enhanced, but integration complexity and operational costs increase
Solution Approach 1:
The system divides the integration process into discrete, manageable segments handled by specialized modules: initialization module for configuration, monitor module for performance tracking, and management module for resource allocation. This segmentation simplifies the overall integration complexity by distributing tasks to dedicated components.
Solution Approach 2:
The initialization module serves multiple functions including configuration, validation, and registration of new components. The management module handles both resource allocation and performance monitoring. This multi-functionality reduces the number of separate systems needed, simplifying integration while enabling capability expansion.
Data Source
AI summary
A modularized computing system includes a plurality of modular components that are coupled together forming a network. Each modular component includes a standard network interface. The system further includes an initialization module, a monitor module, a storage medium, and a management module. As a module unit is coupled to the network, the initialization module automatically configures the component to an operable state. The monitor module monitors network operations including performance parameters of each modular component based on a plurality of system policies. Based on information gathered by the monitor module, the management module actively modifies network structure and resource allocation to optimize network performance.