Virtual machine working queue and redundant queue updating method for different aging scenes

Abstract

The invention provides a virtual machine working queue and redundant queue updating method for different aging scenes, and relates to the technical field of cloud computing. The method comprises the following steps: firstly, dividing different software aging scenes according to the survival time and load fluctuation conditions of the virtual machines, and then dynamically adjusting the number andsequence of working virtual machine copies by adopting a ridge regression-based virtual machine working queue dynamic updating method; and finally, dynamically updating the redundancy queue of the virtual machine based on the binary decision diagram. According to the virtual machine working queue and redundant queue updating method for different aging scenes, the service quality and the resource cost of the virtual machine are balanced through selection and switching strategies, the service quality of the system is guaranteed, and even if the working virtual machine has service failure, the redundant virtual machine can be switched in a short time to completely replace the service failure virtual machine.

Description

technical field [0001] The invention relates to the technical field of cloud computing, in particular to a method for updating a virtual machine work queue and a redundant queue facing different aging scenarios. Background technique [0002] With the widespread application of cloud computing technology, the cloud environment is more complex and difficult to control. On the one hand, cloud service providers need to do their best to ensure the service quality of the system and reduce the number of violations of service agreements; on the other hand, they need to improve resource utilization and reduce service cost. In order to achieve the above goals, real-time monitoring of cloud environment changes and dynamic adjustment of cloud resources are the most effective ways. In the process of cloud resource adjustment, the software aging of virtual machines and the number of concurrent business visits are two factors that cannot be ignored. The software aging problem in the cloud...

AI Technical Summary

Problems solved by technology

In the above-mentioned cloud resource adjustment methods, there are still some deficiencies. The adjustment methods proposed by the predecessors often assume that the running status of the working virtual machine does not change when evaluating the performance of cloud services, and lack of full consideration of the aging of the virtual machine software. Obviously, This type of evaluation method is relatively rough, especially in some long-running cloud service systems, which may cause large deviations; in addition, previous methods generally deal with software aging by setting static thresholds, and only take preventive measures for virtual machines above the aging threshold , and once the services of other working virtual machines fail, the cloud service system cannot make immediate adjustments, which affects the normal access of users and cannot continuously guarantee the reliability of cloud service system services

Moreover, the cloud resource adjustment method proposed by the predecessors lacks consideration of software aging when selecting and adjusting the target virtual machine, and cannot guarantee that the virtual machine with high software aging degree will be restarted in time, which greatly reduces the performance and reliability of the system and increases the Operating costs of the system

[0004] In summary, the cloud resource adjustment method proposed by the predecessors lacks consideration of software aging, which may cause problems such as poor adjustment effect and unguaranteed service quality.

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