Virtual Machine Cluster Elastic Scaling via Configuration Levels
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Solution Overview
Problem
Existing methods for elastically scaling virtual machine clusters face delays due to monitoring overhead and inefficiencies in resource utilization, as all virtual machines have the same configuration level, making it difficult to maximize resource utilization and reduce costs.
Innovation Solution
A method and apparatus that calculate residual resources in a virtual machine cluster, determine the required configuration level for a target virtual machine based on resource demands, and deploy services accordingly, allowing for the use of virtual machines with different configuration levels to optimize resource allocation and reduce costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If virtual machines in a cluster have the same configuration level, then the system is simple to manage, but the resource utilization rate cannot be maximized
Solution Approach 1:
The patent applies local quality by allowing different virtual machines within the same cluster to have different configuration levels (high, medium, low) based on their specific service requirements. Instead of uniform configuration, each virtual machine's resources are tailored to its workload characteristics, enabling high-resource services to receive adequate resources while low-resource services consume fewer resources, thereby maximizing overall cluster resource utilization.
2Measurement precision
If monitoring is used to detect threshold exceedance, then scaling decisions are data-driven, but delay is introduced due to monitoring overhead
Solution Approach 1:
The patent implements preliminary action by pre-establishing multiple configuration levels for virtual machines and pre-defining resource thresholds for each level. When scaling is needed, the system can immediately transition to a predetermined configuration level without waiting for continuous monitoring cycles to detect threshold violations, thereby reducing scaling delay while maintaining data-driven decision accuracy.
3Quantity of substance
If new virtual machines are created to meet resource demand, then resource capacity increases, but creation time adds to scaling delay
Solution Approach 1:
The patent applies copying by creating virtual machine templates for each configuration level (high, medium, low) in advance. When scaling is required, the system instantiates a copy of the appropriate template rather than creating a virtual machine from scratch, significantly reducing creation time while ensuring the new virtual machine has the correct configuration for its intended workload.
4Loss of energy
If virtual machines are removed from the cluster, then costs are reduced, but service continuity may be affected
Solution Approach 1:
The patent implements dynamics by enabling flexible transitions of virtual machines between different configuration levels based on real-time service demand. When reducing costs, the system can dynamically downgrade virtual machines to lower configuration levels or migrate services between virtual machines of different levels, rather than simply removing virtual machines. This dynamic adjustment maintains service continuity while optimizing resource consumption and reducing operational costs.
Data Source
AI summary
The present application discloses a method and apparatus for elastically scaling a virtual machine cluster. A specific implementation of the method includes: calculating a first amount of residual resources of virtual machines in a virtual machine cluster during service deployment; acquiring an amount of resources demanded by a service to be deployed; acquiring a configuration level of the virtual machines in the virtual machine cluster; determining a virtual machine having a predetermined configuration level as a target virtual machine based on whether the first amount of residual resources satisfies the amount of resources demanded; and deploying the service to be deployed to the target virtual machine. This implementation can implement service deployment in a virtual machine cluster including virtual machines having different configuration levels and also can implement the deployment of different services on a single virtual machine, thereby reducing the costs of service deployment and improving the resource utilization rate of the virtual machine cluster.