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Computational Offloading for Energy Constrained Devices in Multi-Hop Cooperative Networks

机译:多跳协作网络中能量受限设备的计算分流

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As the number of mobile devices that natively support ad hoc communication protocols increase, large ad hoc networks can be created not only to facilitate communication among the mobile devices, but also to assist devices that are executing computationally intensive applications. Prior work has developed computation offloading systems for mobile devices, but this work has focused exclusively on offloading to single hop neighbors, due in part to the practical challenges of setting up multi-hop networks using existing ad hoc communication protocols. However, limiting the offloading of computation to one-hop neighbors inherently restricts the number of devices that can participate in the distributed computation. By presenting a heuristic, aimed at avoiding partitioning the network, as well as an iterative task assignment algorithm that can optimize the assignment of computational tasks to devices in a multi-hop cooperative network, we are able to evaluate the effect of computational offloading in multi-hop networks. Experimental results, obtained from an implementation on Android devices, are integrated with an analytical model that enables the evaluation of system performance under a variety of conditions. These experimental and analytic results demonstrate the benefit of enabling computation offloading to all devices in a multi-hop cooperative network.
机译:随着本机支持ad hoc通信协议的移动设备数量的增加,不仅可以创建大型ad hoc网络,以促进移动设备之间的通信,还可以辅助执行计算密集型应用程序的设备。先前的工作已经开发了用于移动设备的计算卸载系统,但是该工作仅专注于卸载到单跳邻居,部分原因是使用现有的自组织通信协议来建立多跳网络的实际挑战。但是,将计算的卸载限制在单跳邻居中会固有地限制可以参与分布式计算的设备数量。通过提出一种旨在避免对网络进行分区的启发式方法,以及可以优化多跳协作网络中设备的计算任务分配的迭代任务分配算法,我们能够评估多任务计算分流的效果跳网络。从Android设备上的实施获得的实验结果与分析模型集成在一起,该模型能够评估各种条件下的系统性能。这些实验和分析结果证明了在多跳协作网络中使计算分流到所有设备的好处。

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