Boot-Time Configuration System for Cloud Node Clusters
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Solution Overview
Problem
Current cloud computing systems lack mechanisms for automating the configuration, deployment, and performance analysis of node clusters, leading to complex and time-consuming processes for users, who often have limited control over hardware resources and network configurations, resulting in inefficient resource utilization and high costs.
Innovation Solution
A method and system that provide a user interface for selecting and modifying boot-time configurations, network settings, and hardware configurations of cloud computing nodes, allowing for automated deployment and performance monitoring across a cluster, enabling users to optimize node configurations based on desired performance characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual configuration of boot-time parameters is performed on each node individually, then configuration flexibility is maintained, but configuration time and complexity increase significantly
Solution Approach 1:
The system segments the configuration process into template definition (separate from nodes) and node-specific parameter assignment. The boot-time parameter configuration is divided into node-type level templates and individual node instantiations, allowing centralized management while maintaining node-level flexibility through parameter substitution.
Solution Approach 2:
The system performs preliminary configuration by creating reusable boot-time parameter templates that define common settings for groups of nodes. These templates are prepared in advance and can be automatically applied to multiple nodes, eliminating the need to manually configure each node individually while preserving the ability to customize specific parameters per node.
2Productivity
If automated configuration deployment is implemented across the cluster, then deployment speed increases, but control over individual node parameters decreases
Solution Approach 1:
The configuration system is designed to be dynamic, allowing users to select and modify specific parameters for individual nodes or groups of nodes even after template deployment. The system supports runtime adjustments and node-specific parameter overrides, enabling both automated deployment and granular control without requiring complete reconfiguration.
3Measurement precision
If comprehensive performance monitoring is implemented across all nodes, then system optimization capability improves, but data aggregation complexity increases
Solution Approach 1:
The system implements a universal performance monitoring framework that collects standardized metrics across all nodes through a common interface. The monitoring agent deployed on each node performs multiple functions (CPU, memory, disk, network monitoring) and reports to a centralized server that aggregates and analyzes data uniformly, reducing complexity while maintaining comprehensive measurement capability.
Data Source
AI summary
The present disclosure relates to a method and system for configuring a computing system, such as a cloud computing system. A method includes providing a user interface comprising selectable boot-time configuration data and selecting, based on at least one user selection of the boot-time configuration data, a boot-time configuration for at least one node of a cluster of nodes of the computing system. The method further includes configuring the at least one node of the cluster of nodes with the selected boot-time configuration to modify at least one boot-time parameter of the at least one node.