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GA-Based Resource Augmentation Negotation for Energy-Optimised Mobile Ad-hoc Cloud

机译:基于GA的能量优化移动ad-hoc云的资源增强协商

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Mobile and cloud computing are two areas which are rapidly expanding in terms of use case and functionality. An ad-hoc mobile cloud can be constructed upon local devices, where devices work together and share resources, aspects such as energy and costs may be minimised. This paper builds on previous work which proposed an end-to-end system whereby user applications may be profiled for their resource consumption locally and then if augmentation is required, they may negotiate with a local mobile ad-hoc cloud for optimum energy and resource utilisation. The contribution of this paper focuses upon a novel and adapted Genetic Algorithm approach to resource negotiation in order to further enable energy-optimised resource augmentation for Mobile Ad-hoc clouds.
机译:移动和云计算是在使用情况和功能方面正在快速扩展的两个区域。可以在本地设备上构建ad-hoc移动云,其中设备一起工作并共享资源,可以最小化诸如能量和成本的方面。本文构建了以前的工作,该工作提出了一个端到端系统,可以在本地的资源消耗中分布用户应用程序,然后如果需要增强,则可以与本地移动ad-hoc云协商以获得最佳能量和资源利用率。本文的贡献侧重于新颖和适应的遗传算法来资源协商方法,以便进一步实现移动广告云的能量优化的资源增强。

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