P&P: A Combined Push-Pull Model for Resource Monitoring in Cloud Computing Environment

He Huang, Liqiang Wang
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引用次数: 70

Abstract

Cloud computing paradigm contains many shared resources, 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 environment. It is the foundation of many major Cloud computing operations. In this paper, we extend the prevailing monitoring 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.
P&P:云计算环境下资源监控的组合推拉模型
云计算范式包含许多共享资源,如基础设施、数据存储、各种平台和软件。资源监控包括收集系统资源的信息,以方便云环境中其他组件的决策。它是许多主要云计算操作的基础。在本文中,我们将网格计算中流行的监控方法,即Pull模型和Push模型,扩展到云计算的范例中。在网格计算中,我们发现在一定条件下,Push模型一致性高但效率低,而Pull模型一致性低但效率高。基于两种模型的互补性,本文提出了一种面向用户的云计算资源监控模型——推拉(P&P)模型,该模型同时采用了上述两种模型,并根据用户的需求和被监控资源的状态智能切换两种模型。实验结果表明,与原有模型相比,P&P模型降低了更新成本,满足了用户对监控组件和监控资源一致性的各种要求。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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