传感器云内虚拟化的动态和自适应数据缓存机制

Subarna Chatterjee, S. Misra
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引用次数: 26

摘要

这项工作提出了一种动态的、自适应的缓存机制,用于传感器云的高效虚拟化——这是该方向的首次尝试之一。该工作引入了内部和外部缓存技术,以确保底层物理网络的资源利用效率。传统的数据传输技术包括向云端的周期性数据包传输。但是,物理环境的变化速度可能不是合理的显著,从而导致冗余的数据包传输和网络资源的低效率利用。所提出的缓存机制可以灵活地适应物理环境的变化速率。结果表明,与现有技术相比,缓存在能耗和网络生命周期方面显著节省了网络资源,分别节省了37.1%和48.43%。实验结果还表明,使用缓存技术,通过传感器云提供给最终用户的数据最新率至少为85.91%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Dynamic and adaptive data caching mechanism for virtualization within sensor-cloud
This work proposes a dynamic, and adaptive caching mechanism for efficient virtualization in sensor-cloud - one of the first attempts in this direction. The work introduces both internal and external caching techniques to ensure efficiency in resource utilization of the underlying physical network. Conventional data transmission techniques involve periodic packet transmissions to the cloud-end. However, the rate of change of the physical environment may not be reasonably significant, thereby leading to redundant packet transmissions and inefficient utilization of network resources. The proposed caching mechanism is flexible with the varied rate of change of the physical environment. Results show that compared to the existing techniques, caching appreciably conserves network resources in terms of energy consumption and network lifetime, by 37.1%, and 48.43%, respectively. Experimental results also depict that using caching techniques, the data provisioned to the end-users through sensor-cloud are atleast 85.91% recent.
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