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Data offloading and task allocation for cloudlet-assisted ad hoc mobile clouds

机译:云辅助Ad hoc移动云的数据分载和任务分配

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摘要

Nowadays, although the data processing capabilities of the modern mobile devices are developed in a fast speed, the resources are still limited in terms of processing capacity and battery lifetime. Some applications, in particular the computationally intensive ones, such as multimedia and gaming, often require more computational resources than a mobile device can afford. One way to address such a problem is that the mobile device can offload those tasks to the centralized cloud with data centers, the nearby cloudlet or ad hoc mobile cloud. In this paper, we propose a data offloading and task allocation scheme for a cloudlet-assisted ad hoc mobile cloud in which the master device (MD) who has computational tasks can access resources from nearby slave devices (SDs) or the cloudlet, instead of the centralized cloud, to share the workload, in order to reduce the energy consumption and computational cost. A two-stage Stackelberg game is then formulated where the SDs determine the amount of data execution units that they are willing to provide, while the MD who has the data and tasks to offload sets the price strategies for different SDs accordingly. By using the backward induction method, the Stackelberg equilibrium is derived. Extensive simulations are conducted to demonstrate the effectiveness of the proposed scheme.
机译:如今,尽管现代移动设备的数据处理能力得到了快速发展,但是资源在处理能力和电池寿命方面仍然受到限制。某些应用程序,尤其是计算密集型应用程序,例如多媒体和游戏,通常需要比移动设备所能承受的更多的计算资源。解决此问题的一种方法是,移动设备可以将这些任务转移到具有数据中心,附近的cloudlet或自组织移动云的集中式云中。在本文中,我们提出了一种用于由Cloudlet辅助的自组织移动云的数据分载和任务分配方案,其中具有计算任务的主设备(MD)可以从附近的从设备(SD)或Cloudlet访问资源,而不是集中式云,以共享工作量,以减少能耗和计算成本。然后制定了两阶段的Stackelberg游戏,其中SD决定了他们愿意提供的数据执行单元的数量,而拥有数据和任务要卸载的MD则相应地为不同SD设置了价格策略。通过使用反向归纳法,推导出斯塔克尔伯格平衡。进行了广泛的仿真,以证明所提出方案的有效性。

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