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Optimal Joint Power Allocation and Task Splitting in Wireless Distributed Computing

机译:无线分布式计算中的最优联合功率分配和任务分配

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The mobile application market is growing very fast. More and more applications require intensive computations. However, computation power of mobile devices is limited and does not catch up with the growth of computation demand of applications. Computation offloading is a promising approach for reducing the computation load and reducing execution time and energy consumption. In this work, we investigate a wireless distributed computing scenario where a mobile device exploits the computation resources of the nearby devices to reduce execution time. The main goal is to study the gain of computation offloading among heterogeneous devices. We formulate the problem of optimal offloading as a joint resource allocation problem consisting of power allocation and task splitting. The resulting problem is non-linear and non-convex. By exploiting the unique characteristics of the problem, we can transform it into a convex problem. The optimal solution of the original problem can then be determined by performing a bisection search over one parameter. Using numerical simulation, we can show that by offloading computation load to nearby devices, the execution time is reduced substantially. Moreover, the gain of offloading in comparison with computing locally increases with the ratio of the computation load to the size of input data of the task as well as with increasing number of nearby devices.
机译:移动应用市场正在快速增长。越来越多的应用程序需要大量的计算。然而,移动设备的计算能力是有限的,并且不能赶上应用程序的计算需求的增长。计算分流是一种有希望的方法,可以减少计算量并减少执行时间和能耗。在这项工作中,我们研究了一种无线分布式计算方案,其中移动设备利用附近设备的计算资源来减少执行时间。主要目标是研究异构设备之间计算分流的收益。我们将最佳卸载问题公式化为由功率分配和任务拆分组成的联合资源分配问题。产生的问题是非线性和非凸的。通过利用问题的独特特征,我们可以将其转化为凸问题。然后可以通过对一个参数执行二等分搜索来确定原始问题的最佳解决方案。使用数值模拟,我们可以证明,通过将计算负荷转移到附近的设备,可以大大减少执行时间。此外,与本地计算相比,卸载的增益随计算负载与任务输入数据大小的比率以及附近设备数量的增加而增加。

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