Resource self-adaptive adjusting system and method of multiple virtual machines under single physical machine

Abstract

The invention provides a resource self-adaptive adjusting system and method of multiple virtual machines under a single physical machine. The system is achieved in the single physical machine and comprises a data collecting module, a preprocessing module, a prediction module, a resource adjustment strategy generating module, a resource adjustment strategy gain calculating module, a monitoring module, a strategy evaluation module and a historical database. The method comprises the steps that the historical data of a server are collected and stored in the historical database; the historical data of the server is preprocessed; the concurrent user request amount at the next moment is predicted, and then the demand amount prediction intervals of virtual machine resources is obtained by the predicted value of the concurrent user request amount; an optimal resource adjusting strategy is determined; CPU resource adjustment and internal storage resource adjustment are conducted; the optimal resource adjusting strategy is evaluated; the current optimal resource adjustment strategy and the evaluation value of the current optimal resource adjustment strategy are stored into the historical database. According to the resource self-adaptive adjusting system and method of the multiple virtual machines under the single physical machine, the resource amounts of all the virtual machines on the single physical machine can be adjusted to be adaptive to dynamically-changed resource demands, and therefore the resources of the single physical machine can obtain the biggest benefits.

Description

technical field [0001] The invention belongs to the technical field of cloud services, and in particular relates to a system and method for resource adaptive adjustment of multiple virtual machines under a single physical machine. Background technique [0002] Cloud computing is a new type of computing model following parallel computing, distributed computing, and grid computing. In a cloud environment, multiple virtual machines (VMs) are usually deployed on a resource pool, and these virtual machines share physical resources (CPU, memory, etc.) on this node. Each virtual machine serves as a server to publish services, and users access the services through the Web and consume a certain amount of physical resources of the server. [0003] When configuring a virtual machine, you must specify the number of various resources of the virtual machine. Since the concurrent user requests of each virtual machine are constantly changing, each virtual machine has different requirements...

AI Technical Summary

Problems solved by technology

When resources are insufficient, most researchers simply divide VMs into several levels and allocate resources according to weights, without considering the dependence of service attributes on specific resources, and the division of levels is not authoritative enough.

The time effect of resources is not considered in resource allocation, and the impact of adjustment cycles on overall VM performance is also ignored

In addition, the creation, deletion, and migration of virtual machines will take a certain amount of time and interact with other activities on the node, resulting in increased delays in virtual machine creation, deletion, and migration, and degraded application performance

Large-scale virtual machine migration will lead to huge system overhead, and it will take a long time to complete the entire migration. Frequent virtual machine relocation under the scale of cloud computing infrastructure is difficult to achieve good results in practice

The most important thing is that without any processing on the traditionally selected empirical data, decisions with better effects cannot be retained.

Images