Virtual machine scheduling method applied to cloud computing environment

Abstract

The invention discloses a virtual machine scheduling method applied to a cloud computing environment. The method comprises: 1) modeling of the virtual machine: pre-creating multiple virtual machines; 2) new task scheduling: searching for virtual machines with processing capabilities matched with tasks based on sizes of tasks among pre-created virtual machines; 3) processing of new tasks: processing tasks by searched virtual machines. The virtual machine scheduling method applied to the cloud computing environment has following beneficial effects: virtual machines can be matched with tasks so that reasonable utilization of cloud resources is achieved; and service quality of cloud computing is increased.

Description

technical field [0001] The invention relates to the field of cloud computing. Background technique [0002] Cloud computing is a new computing model after distributed computing, parallel computing, and grid computing, and has attracted the attention of academia and industry. At present, the goal of task scheduling in the cloud computing environment is to reasonably deploy user needs and improve the economic benefits of operators. Specifically reflected in the following goals: optimal time span, load balancing, service quality, and economic principles. [0003] In the existing research on scheduling strategies, the classic scheduling algorithms have better performance on specific scheduling objectives. The classic scheduling algorithm for load balancing includes the Opportunistic Load Balancing algorithm (Opportunistic Load Balancing), which can well solve the scheduling problem caused by unbalanced load in the grid environment. Minimum Execution Time algorithm (Minimum Ex...

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Problems solved by technology

[0005] Due to the different processing capabilities of each node in cloud computing, each task occupies different resources. It is difficult to dynamically create a virtual machine that matches the resources occupied by the task, and it takes more time.

However, if the virtual machine is created in advance, the scheduling will face the following two problems: 1) When a large task is assigned to a virtual machine node with weak processing capacity, the node processing time will be longer or even unable to complete, resulting in the entire task sequence 2) When a smaller task is assigned to a virtual machine node with stronger processing capacity, the throughput of the entire cloud system is reduced due to the large task being in a waiting state while processing the small task

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