Virtual Machine Migration for Service Continuity
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Solution Overview
Problem
Conventional resource adjustment methods for virtual machines often require shutting down VMs to reallocate resources, leading to service interruptions and operational inconveniences, especially when resources are insufficient to meet user demands.
Innovation Solution
A resource adjustment system comprising a monitoring module, estimating module, and moving module that assesses server and virtual machine performance and status information to dynamically redistribute resources without shutting down VMs by identifying suitable virtual machines to move to other servers based on evaluation scores and service priorities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If virtual machine resources are adjusted by shutting down VMs, then resource reallocation can be performed, but service continuity is interrupted and operational convenience deteriorates
Solution Approach 1:
The system performs preliminary actions by creating snapshot copies of virtual machine states before resource adjustment. This allows the VM to be migrated or resized without shutting down, as the snapshot captures the current state and enables restoration if needed. The monitoring module continuously tracks VM status during this preliminary phase.
Solution Approach 2:
The patent employs copying by creating virtual machine snapshots that replicate the VM's state at a specific moment. These snapshots serve as copies that can be used for migration or rollback purposes, enabling resource adjustment without interrupting the original VM's operation. The copying mechanism ensures service continuity while allowing resource reallocation.
2Productivity
If virtual machine resources are increased to meet user demands, then service quality improves, but server load increases and may require additional hardware resources
Solution Approach 1:
The system implements dynamic resource allocation where the monitoring module continuously assesses server and VM performance metrics. Based on this real-time data, the estimating module dynamically adjusts resource distribution, allowing services to receive increased resources when needed while automatically reducing allocations when demand decreases, thus optimizing service quality without permanently increasing server resource consumption.
Solution Approach 2:
The patent changes the parameter of resource allocation from static to dynamic by continuously monitoring performance metrics and adjusting resource distribution accordingly. The system modifies resource parameters (CPU, memory, storage) based on actual service demands and server capacity, enabling service quality improvement while preventing permanent resource over-provisioning.
3Measurement precision
If performance monitoring and evaluation is performed in real-time, then resource adjustment accuracy improves, but system complexity and processing overhead increase
Solution Approach 1:
The system segments the complex monitoring and evaluation process into distinct functional modules: a monitoring module that collects performance data, an estimating module that processes this data against predefined criteria, and a moving module that executes resource adjustments. This segmentation reduces system complexity by organizing functions into manageable, independent components while maintaining real-time monitoring precision.
Solution Approach 2:
The patent implements feedback mechanisms where the monitoring module continuously provides performance data to the estimating module, which compares current state against target criteria and generates adjustment recommendations. This closed-loop feedback system improves measurement precision by continuously refining resource allocation decisions based on actual performance data while automating the process to manage complexity.
Data Source
AI summary
A resource adjustment system and method for virtual machines (VMs) are provided. The method includes: obtaining, by a monitoring module, performance and status information of a first server and at least one VM of the first server; estimating, by an estimating module, the first server and each VM according to the performance and status information and an evaluation index and obtaining a server estimation score of the first server and a virtual machine estimation score of every VM; and finding, by a moving module, a second server according to the performance and status information and the evaluation index when a virtual machine estimation score of at least one first VM of the first server or the server estimation score exceeds a threshold, finding at least one VM suitable to be moved according to a moving order, and moving the at least one VM to the second server.