A dynamic adjustment method for energy saving of virtual web application clusters in cloud computing environment

Abstract

The invention relates to an energy-saving dynamic adjustment method of a virtual web application cluster in a cloud computing environment. The energy-saving dynamic adjustment method includes: a building the virtual web application cluster; b monitoring the use condition of a virtual web application server; c judging the conditions of average central processing unit (CPU) use ratio and average memory use ratio in a section of [min%, max%]; d continuing to monitor the virtual web application server if both the CPU use ratio and average memory use ratio are in the section of [min%, max%]; e using a dynamical expanding method to build a new server if one of the CPU use ratio and average memory use ratio is larger than max% and the other of the CPU use ratio and average memory use ratio is larger than min%; f using a dynamic reduction method to remove a server on a host machine with the lowest resource use equivalent if one of the CPU use ratio and average memory use ratio is smaller than min% and the other of the CPU use ratio and average memory use ratio is smaller than max%; and g using a dynamic specification variation expanding method to build a new server if one of the CPU use ratio and average memory use ratio is smaller than min% and the other one of the CPU use ratio and average memory use ratio is larger than max%. The energy-saving dynamic adjustment method adjusts the web application cluster through the dynamical expanding method, the dynamic reduction method or the dynamic specification variation expanding method, ensures concentrated and lowest quantity operation of the host machine in a cloud platform, and achieves effective energy saving effect.

Description

technical field [0001] The present invention relates to a dynamic adjustment method for energy saving of a virtual web application cluster in a cloud computing environment, and more specifically, to a real-time dynamic expansion, reduction, or specification expansion server according to the resource usage status of an existing virtual web application server A dynamic adjustment method for energy saving of virtual web application clusters in cloud computing environment. Background technique [0002] Cloud computing is a computing model that provides dynamic and scalable virtualized resource services through the network. It is another great change in the computing model after the transformation from the mainframe computer model to the server / client model. Cloud computing aims to integrate multiple relatively low-cost computing entities into a system with powerful computing capabilities through the network, so it can provide users with low-cost computing services without the ne...

AI Technical Summary

Problems solved by technology

[0004] However, in the current virtualization technology commonly used in cloud computing, the virtual web application cluster provided is not automatically adjusted dynamically according to the load of the user's web application. Users need to track the resource usage of the virtual web application cluster in real time, and then Manually scale up or down the cluster based on load

Especially when the user's web application has a sudden load, the response speed of the user's manual adjustment is often not so timely, which causes the user's web application to be hindered due to excessive load

On the other hand, if the user does not plan well in advance for the load of the virtual web application cluster, resulting in a large virtual web application cluster, but the actual load of the cluster is small, or the user estimates the cluster load inaccurately, the created The virtual machine specification does not adapt to the load, resulting in high CPU utilization and low memory utilization, or low CPU utilization and high memory utilization, causing more CPU or memory to be idle but the user load problem remains unresolved. Make more computing resources idle in the cluster, resulting in greater energy waste

Images