Virtual machine arrangement scheduling method and equipment

By creating virtual machine planning models and optimizing deployment solutions in a cloud computing environment, the problem of virtual machine resource fragmentation is solved, resource utilization and orchestration and scheduling efficiency are improved, and costs are reduced.

CN119718549APending Publication Date: 2025-03-28BEIJING VOLCANO ENGINE TECH CO LTD +1
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Patent Information

Application Number
CN202411911576.3
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-12-23
Publication Date
2025-03-28

AI Technical Summary

Technical Problem

The existing technology is difficult to effectively solve the problem of virtual machine resource fragmentation in cloud computing environments, resulting in low resource utilization and high orchestration and scheduling costs.

Method used

By responding to the VM secondary scheduling request, creating a virtual machine planning model, determining the deployment plan with the lowest total scheduling cost, considering the number of deployments predicted without taking into account the VM anti-affinity constraints, and creating a second deployment plan for virtual machines with anti-affinity constraints to optimize their scheduling costs.

Benefits of technology

It improves the efficiency and success rate of virtual machine orchestration and scheduling, improves resource utilization, and reduces orchestration and scheduling costs.

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Abstract

The embodiment of the invention provides a virtual machine arrangement and scheduling method and equipment, and the method comprises the steps: creating a first virtual machine planning model according to the deployment attribute characteristics of an initial virtual machine deployed on a physical machine in response to a secondary scheduling request of a virtual machine, and determining a first deployment scheme enabling the total scheduling cost to be lowest; comprising the following steps: on the premise of not considering the anti-affinity constraint of the virtual machines, predicting the deployment number of each type of initial virtual machines in each physical machine, and determining to-be-migrated initial virtual machines in each type of initial virtual machines in each physical machine; creating a second virtual machine planning model for the target to-be-emigrated initial virtual machine with the anti-affinity constraint, and determining a second deployment scheme enabling the scheduling cost of the target to-be-emigrated initial virtual machine to be the lowest, including a target physical machine to which each target to-be-emigrated initial virtual machine is to be deployed on the premise of considering the anti-affinity constraint of the virtual machine; and performing virtual machine scheduling. Through a multi-stage scheduling simplified model, the scheduling efficiency and success rate of the virtual machine are improved.
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