Cluster Resource Pool Binding for Cross-Application Scaling
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Solution Overview
Problem
Existing resource control methods in cloud systems lack applicability across different business applications, leading to inefficiencies in resource expansion and contraction.
Innovation Solution
A method and apparatus for controlling cluster resources by determining binding relationships, initializing resources into application-specific pools, generating deployment packets based on application types, and deploying these packets on resources while managing SSH connections and post-scripts to ensure smooth expansion and contraction processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If resource control methods are customized for specific applications and scenes, then the effectiveness for that specific application is improved, but the applicability to other applications deteriorates
Solution Approach 1:
The patent creates a universal resource control system that can handle multiple application types (batch applications, on-demand applications, map-reduce applications) through a unified framework. The system uses standardized interfaces and common processing logic that adapts to different application scenarios, allowing the same resource control methodology to be applied across diverse cloud computing applications without requiring custom development for each specific case.
2Ease of operation
If manual resource control operations are performed by operation and maintenance personnel, then flexibility in decision-making is improved, but operation time and labor costs increase
Solution Approach 1:
The patent implements automated resource control through the orchestration system that can autonomously determine when resource expansion or contraction is needed based on monitoring metrics, and automatically execute the appropriate control operations. The system self-manages the resource control process without requiring manual intervention from operation and maintenance personnel, thereby eliminating time loss while maintaining operational effectiveness through algorithmic decision-making.
Solution Approach 2:
The system continuously monitors resource usage metrics and application performance, using this feedback information to automatically trigger resource control operations when thresholds are exceeded or optimal conditions change. This closed-loop control mechanism enables the system to respond dynamically to changing conditions without manual intervention, reducing both time loss and labor requirements while maintaining flexible, data-driven decision-making.
Data Source
AI summary
This disclosure relates to a control method and apparatus of cluster resources, and a cloud computing system, and relates to the field of computer technologies. The method includes: in the case where a to-be-controlled resource is a to-be-expanded resource, determining a binding relationship between the to-be-expanded resource and an application; adding the to-be-expanded resource that is initialized into a resource pool of a corresponding application having the binding relationship with the to-be-expanded resource; generating a to-be-executed data packet of a to-be-processed application according to a deployment type of the to-be-processed application; and deploying the to-be-executed data packet on the to-be-expanded resource in the resource pool of the to-be-processed application for execution.