Markov Based Computational Tasks Offloading Decision for Face Detection

Mi Swe Zar Thu, Ei Chaw Htoon
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引用次数: 1

Abstract

Smart mobile devices are essential for technology trend of modern lifestyles. Mobile applications are getting more diverse and complex with an increasing use of mobile devices. At mobile environment, resource limitation is irresistible extremely; it is a key challenge and can impact on mobile computing performance. Mobile Cloud computing (MCC) is one of the eventual gold computing executions of rich mobile application on an abundance of mobile devices. Limited computational power, storage, and energy are necessary for hardware limitations of offloading. So, the proposed system aims to avoid limitation of devices starting in case of intensive tasks by using dynamic computation offloading of Markov process. The system reduces energy consumption of resource hungry device as the way of taking decision to offload the computing intensive tasks to the remote cloud by the result of our cost model. The experiment shows that the proposed offloading decision solver can reduce not only computing time but also battery usage of mobile device for face detection application compared to the dynamic off loading MAUI decision framework.
基于马尔可夫的人脸检测任务卸载决策
智能移动设备是现代生活方式的技术趋势所必需的。随着移动设备使用的增加,移动应用程序变得越来越多样化和复杂。在移动环境下,资源的有限性是不可抗拒的;这是一个关键的挑战,可能会影响移动计算性能。移动云计算(MCC)是丰富移动应用程序在大量移动设备上的最终黄金计算执行之一。有限的计算能力、存储和能量对于卸载的硬件限制是必要的。因此,该系统旨在通过马尔可夫过程的动态计算卸载来避免任务密集时设备启动的限制。该系统通过成本模型的计算结果,降低了资源密集型设备的能耗,将计算密集型任务转移到远程云上。实验表明,与动态卸载MAUI决策框架相比,所提出的卸载决策求解器不仅可以减少人脸检测应用的计算时间,还可以减少移动设备的电池使用。
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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