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PP: a Combined Push-Pull Model for Resource Monitoring inCloud Computing Environment

机译:P&P:用于资源监控的组合推挽模型,欢迎计算环境

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Cloud computing paradigm contains many shared re-sources, such as infrastructures, data storage, various platforms and software. Resource monitoring involves collecting information of system resources to facilitate decision making by other components in Cloud environ-ment. It is the foundation of many major Cloud computing operations. In this paper, we extend the prevailing moni-toring methods in Grid computing, namely Pull model and Push model, to the paradigm of Cloud computing. In Grid computing, we find that in certain conditions, Push model has high consistency but low efficiency, while Pull model has low consistency but high efficiency. Based on complementary properties of the two models, we propose a user-oriented resource monitoring model named Push&Pull (P&P) for Cloud computing, which employs both the above two models, and switches the two models intelligently according to users requirements and monitored resources status. The experimental result shows that the P&P model decreases updating costs and satisfies various users requirements of consistency between monitoring components and monitored resources compared to the original models.
机译:Cloud Computing PARADIGM包含许多共享重新源,例如基础架构,数据存储,各种平台和软件。资源监控涉及收集系统资源的信息,以便于云环境中的其他组件的决策。这是许多主要云计算操作的基础。在本文中,我们在网格计算中延长了普遍的单抗体方法,即拉模型和推动模型,云计算范例。在网格计算中,我们发现在某些条件下,推动模型具有高一致性但效率低,而拉模型具有低一致性但高效率。基于两种型号的互补特性,我们提出了一种以上述两种型号的云计算命名为推送和拉动(P&P)的面向用户的资源监测模型,并根据用户要求智能地切换两种型号。实验结果表明,P&P模型降低了更新成本,并满足了与原始模型相比,监控组件与监控资源之间的一致性的需求。

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