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DewSim: A trace-driven toolkit for simulating mobile device clusters in Dew computing environments

机译:DewSim:跟踪驱动的工具包,用于在Dew计算环境中模拟移动设备集群

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摘要

Dew computing is an emerging computing paradigm, which aims at minimizing the dependency over existing internetwork back-haul, ie, being dependent on processing resources offered by remote servers. Smartphones and tablets ubiquity and powerful computing hardware motivated researchers to investigate the way of providing Dew computing services by exploiting the aggregated capabilities of devices in a vicinity, a smart device cluster. Consequently, research on resource management is necessary to learn how to scavenge resources from such a cluster, deal with devices heterogeneity, limitations, and dynamic resource availability. Simulation is commonly practiced for studying resource management in other distributed computing research fields, specially due to the complexity involved in the set up of experiments. However, a free-to-use purpose specific toolkit for studying smart device clusters do not exist or have been documented. Current simulation efforts do not allow researchers to faithfully represent key singularities of such environment, which are energy depletion and nondedicated nature of computing resources. We propose a trace-based toolkit built on modular software artifacts to speed up research in resource management techniques in Dew environments. A trace-driven methodology is adopted to assure practical value of simulated scenarios. The toolkit comprises a device profiler application for Android to capture generic battery and CPU traces from real devices, a profile mixer to create user interaction baseline traces through generic ones, and an extensible engine to simulate the execution of workloads configurable via text files. Verification and validation tests were run to show correctness and reliability of our simulation approach.
机译:露水计算是一种新兴的计算范式,其目的是最大程度地减少对现有互联网回程的依赖,即,依赖于远程服务器提供的处理资源。智能手机和平板电脑无处不在以及强大的计算硬件促使研究人员通过利用智能设备集群附近设备的聚合功能来研究提供露水计算服务的方式。因此,必须进行资源管理研究,以学习如何从这样的群集中清除资源,处理设备的异构性,局限性和动态资源可用性。特别是由于实验设置涉及的复杂性,通常在其他分布式计算研究领域中通常采用模拟来研究资源管理。但是,尚不存在用于学习智能设备集群的免费使用的专用工具包,或者已经记录了该工具包。当前的仿真工作不允许研究人员如实地表示这种环境的关键奇点,即能量消耗和计算资源的非专用性质。我们提出基于模块化软件工件的基于跟踪的工具包,以加快露水环境中资源管理技术的研究。采用跟踪驱动的方法来确保模拟方案的实用价值。该工具包包括一个用于Android的设备事件探查器应用程序,用于从实际设备捕获通用电池和CPU跟踪;一个配置文件混合器,用于通过通用跟踪器创建用户交互基线跟踪;一个可扩展引擎,用于模拟可通过文本文件配置的工作负载的执行。运行验证和确认测试以显示我们的仿真方法的正确性和可靠性。

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