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Towards the optimization of power and bandwidth consumption in mobile-cloud hybrid applications

机译:致力于优化移动云混合应用程序中的功耗和带宽消耗

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Mobile devices can now support a wide range of applications, many of which demand high computational power. Backed by the virtually unbounded resources of cloud computing, today's mobile-cloud (MC) computing can meet the demands of even the most computationally and resource intensive applications. However, many existing MC hybrid applications are inefficient in terms of achieving objectives like minimizing battery power consumption and network bandwidth usage, which form a tradeoff. To counter this problem we propose a technique that: 1) measures, at run time, how well the MC application meets these two objectives; and 2) allows arbitrary configurations to be applied to the MC application in order to optimize the efficiency tradeoff. Our experimental evaluation considers two MC hybrid applications. We modularized them first, based on computationally-intensive tasks, and then executed them using a simple MC framework while measuring the power and bandwidth consumption at run-time. Analysis of results shows that efficient configurations of the apps can be obtained in terms of minimizing the two objectives. However, there remain challenges such as scalability and automation of the process, which we discuss.
机译:现在,移动设备可以支持广泛的应用程序,其中许多需要高计算能力。在几乎无限的云计算资源的支持下,当今的移动云(MC)计算可以满足即使是计算量和资源最密集的应用程序的需求。但是,许多现有的MC混合应用程序在实现诸如使电池功耗和网络带宽使用率降至最低等目标方面效率低下,这是一个折衷方案。为了解决这个问题,我们提出了一种技术:1)在运行时测量MC应用程序满足这两个目标的程度; 2)允许将任意配置应用于MC应用,以优化效率权衡。我们的实验评估考虑了两种MC混合应用。我们首先基于计算密集型任务将它们模块化,然后使用简单的MC框架执行它们,同时测量运行时的功耗和带宽消耗。结果分析表明,可以通过最小化两个目标来获得应用程序的有效配置。但是,我们将讨论诸如流程的可伸缩性和自动化之类的挑战。

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