Cluster Capacity Control Terminal for Dynamic Virtual Host Adjustment
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Solution Overview
Problem
Existing cluster systems, particularly those using bare metal and existing Kubernetes clusters, face challenges in adaptively performing capacity reduction or expansion due to limited supportability and compatibility of cloud platforms, failing to dynamically adjust to changes in service loads.
Innovation Solution
A method that acquires performance data from clusters to determine whether capacity expansion or reduction is needed, using virtualization capabilities to create or remove virtual hosts on a cloud platform, enabling dynamic capacity adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If cloud platforms are used to support cluster capacity adjustments, then capacity expansion and reduction capability is improved, but supportability and compatibility remain limited in existing systems
Solution Approach 1:
The patent introduces a capacity reduction and expansion control terminal as an intermediary component between the cloud platform and the cluster. This terminal acquires performance data from the cluster, determines capacity adjustment needs, and controls the cloud platform to create or remove virtual hosts accordingly. The intermediary resolves the contradiction by providing a dedicated control mechanism that ensures reliable and compatible capacity adjustments without requiring changes to the underlying cloud platform infrastructure.
2Adaptability or versatility
If virtualization capabilities are used to create or remove virtual hosts, then dynamic capacity adjustment is improved, but device complexity increases
Solution Approach 1:
The system implements self-service automation where the capacity reduction and expansion control terminal automatically acquires performance data, determines capacity needs, and executes virtual host creation or removal operations. This eliminates the need for manual intervention and complex configuration management, reducing operational complexity while maintaining dynamic adjustment capabilities. The automated feedback loop between performance monitoring and capacity adjustment simplifies the overall system operation.
3Measurement precision
If performance data monitoring is implemented to determine capacity needs, then capacity adjustment accuracy is improved, but measurement and detection complexity increases
Solution Approach 1:
The capacity reduction and expansion control terminal performs multiple functions: it acts as a performance data collector, analyzer, decision-maker, and executor of capacity adjustments. By consolidating these diverse functions into a single multi-functional terminal, the system achieves accurate capacity determination through comprehensive performance monitoring while avoiding the complexity of multiple separate systems. The universal terminal handles various performance metrics and coordination tasks through a unified architecture.
Data Source
AI summary
The present disclosure provides a capacity reduction and capacity expansion control terminal, a computer-readable medium, and a capacity reduction and capacity expansion system for a cluster. A capacity reduction and capacity expansion method for a cluster includes: acquiring performance data of a target cluster; determining whether the target cluster needs capacity expansion or capacity reduction according to the performance data; when it is determined that the target cluster needs the capacity expansion, controlling a cloud platform to create a first virtual host, and adding the first virtual host to the target cluster; and when it is determined that the target cluster needs the capacity reduction, controlling the cloud platform to remove a second virtual host from the target cluster.