Self-adaptation resource monitoring system and method based on large-scale cloud computing platform

A resource monitoring and cloud platform technology, applied in the field of cloud computing, can solve problems such as cloud computing research lag

Active Publication Date: 2012-08-22
GUANGDONG ELECTRONICS IND INST
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0002] Since cloud computing was formally proposed in 2007, it has attracted the attention of the academic community, and it has provided cloud services to users in the form of commercial applications before, and the research on cloud computing in the academic community is relatively lagging behind.

Method used

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  • Self-adaptation resource monitoring system and method based on large-scale cloud computing platform
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  • Self-adaptation resource monitoring system and method based on large-scale cloud computing platform

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Embodiment Construction

[0036] like figure 1 As shown, the resource monitoring system of the present invention includes a G101 resource monitoring module, a G102 resource monitoring and analysis module, a G103 data collection module and a G104 data storage module; the resource monitoring module is used for periodic monitoring according to the resources and parameters of the monitoring template customized by the node. sample and send the data to the data collection module. The resource monitoring and analysis module is used to decide the data transmission mode according to the sampling information of the resource monitoring module. The data collection module is used to collect sampling data of various resources, and then send it to the data storage module in a suitable push method according to a certain mechanism. The data storage module is used for storing the received data according to a predetermined format. The content of the aforementioned monitoring and analysis includes real-time sampling val...

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Abstract

The invention relates to the technical field of cloud computing, and in particular relates to a self-adaptation resource monitoring system and method based on a large-scale cloud computing platform. The method comprises the steps of establishing a multi-clustering system by setting up a cloud environment; acquiring a network topological graph under the cloud environment, and monitoring the state of each node or virtual machine according to the topological graph; and customizing a monitoring parameter configuration template, and monitoring the resource use condition of each node or virtual machine. According to the invention, the monitoring system can be well adaptive to dynamic addition and quit of resources on the cloud platform, the information collected by monitoring can well reflect the load information on the platform. The self-adaptation resource monitoring system and method can be applied to the monitoring of the resources on the cloud computing platform.

Description

technical field [0001] The invention relates to the technical field of cloud computing, in particular to an adaptive resource monitoring system and method based on a large-scale cloud computing platform. Background technique [0002] Since cloud computing was officially proposed in 2007, it has attracted the attention of the academic community. Before, it has provided cloud services for users in a commercial application mode. The research on cloud computing in academia is relatively lagging behind. Cloud computing is the continuation of Distributed Computing, Parallel Computing and Grid Computing. At present, there are three main business application models of cloud computing: IaaS (Infrastructure as a Service: infrastructure as a service), PaaS and SaaS (Software as a Service: software and services). With the characteristics of reliability, high scalability, versatility and low cost, more and more users and enterprises have begun to pay attention to and use the services pr...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H04L29/08
Inventor 陈红岳强季统凯
Owner GUANGDONG ELECTRONICS IND INST
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