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Offloading Optimization and Bottleneck Analysis for Mobile Cloud Computing

机译:移动云计算的卸载优化和瓶颈分析

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Mobile cloud computing systems, or simply mobile clouds, have attracted tremendous attention because they allow mobile devices with limited computational resources to offload complex computations. However, due to the channel uncertainty and the complexity of a computation task, mobile computation offloading may suffer from poor outage performance that the offloaded task cannot be completed within the desired delay constraint. Thus, how to efficiently identify and overcome the outage bottleneck, which could be used to optimize resource allocation schemes and improve the system performance effectively is an open problem. In this paper, we shall develop a unified framework that minimizes the overall outage probability in various mobile computation offloading scenarios. More specifically, the outage bottleneck is defined and identified by adopting asymptotic analysis, without any need of the accurate outage probabilities in both transmissions and computations. To overcome the outage bottleneck, resource pairing, matching, and allocation policies are investigated. Both theoretical analysis and numerical results show that the outage bottleneck relies on not only the availability of spectrum and computation resources but also the probability distributions of computation complexities of the computation tasks.
机译:移动云计算系统(或简称为移动云)已经引起了极大的关注,因为它们允许计算资源有限的移动设备卸载复杂的计算。然而,由于信道不确定性和计算任务的复杂性,移动计算卸载可能遭受中断性能差的影响,即卸载任务无法在期望的延迟约束内完成。因此,如何有效地识别和克服中断瓶颈,从而可以用来优化资源分配方案并有效地提高系统性能是一个悬而未决的问题。在本文中,我们将开发一个统一的框架,以最大程度地降低各种移动计算分流方案中的总体中断概率。更具体地说,通过采用渐近分析来定义和识别中断瓶颈,而在传输和计算中都不需要精确的中断概率。为了克服中断瓶颈,研究了资源配对,匹配和分配策略。理论分析和数值结果均表明,中断瓶颈不仅取决于频谱和计算资源的可用性,还取决于计算任务的计算复杂度的概率分布。

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