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Prioritization of Overflow Tasks to Improve Performance of Mobile Cloud

机译:溢出任务的优先级,提高移动云性能

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摘要

Mobile devices may offload their applications to a virtual machine running on a cloud host. This application may fork new tasks which require virtual machines of their own on the same physical machine. Achieving satisfactory performance level in such a scenario requires flexible resource allocation mechanisms in the cloud data center. In this paper we present two such mechanisms which use prioritization: one in which forked tasks are given full priority over newly arrived tasks, and another in which a threshold is established to control the priority so that full priority is given to the forked tasks if their number exceeds a predefined threshold. We analyze the performance of both mechanisms using a Markovian multiserver queueing system with two priority levels to model the resource allocation process, and a multi-dimensional Markov system based on a Birth-Death queueing system with finite population, to model virtual machine provisioning. Our performance results indicate that the threshold-based priority scheme not only performs better, but can also be tuned to achieve the desired performance level.
机译:移动设备可以将其应用程序卸载到在云主机上运行的虚拟机。此应用程序可能会在同一物理机器上耗时需要自己的虚拟机的新任务。在这种情况下实现令人满意的性能水平需要云数据中心中灵活的资源分配机制。在本文中,我们介绍了两个使用优先级的这样的机制:分叉任务在新到达任务中给出完整优先级的机制,以及建立阈值以控制优先级的另一个机制,以便如果它们的情况下,将完整优先级提供完整优先级数字超过预定义的阈值。我们使用具有两个优先级的Markovian MultiServer排队系统来分析两种机制的性能,以模拟资源分配过程,以及基于具有有限群体的出生死亡排队系统的多维马尔可夫系统,以模拟虚拟机配置。我们的性能结果表明,基于阈值的优先级方案不仅更好地执行,但也可以调整以实现所需的性能水平。

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