Mobile grid computing has been a popular topic for researchers due to mobile and IoT devices’ ubiquity and their evergrowing processing potential. While many scheduling algorithms for harnessing these resources exist in the literature for standard grid computing scenarios, surprisingly, there is little insight into this matter in the context of hybrid-powered computing resources, typically found in Dew and Edge computing environments. This paper proposes new algorithms aware of devices’ power source for scheduling tasks in hybrid environments, i.e., where the battery- and non-battery-powered devices cooperate. We simulated hybrid Dew/Edge environments by extending DewSim, a simulator that models battery-driven devices’ battery behavior using battery traces profiled from real mobile devices. We compared the throughput and job completion achieved by algorithms proposed in this paper using as a baseline a previously developed algorithm that considers computing resources but only from battery-dependent devices called Enhanced Simple Energy-Aware Schedule (E-SEAS). The obtained results in the simulation reveal that our proposed algorithms can obtain up to a 90% increment in overall throughput and around 95% of completed jobs in hybrid environments compared to E-SEAS. Finally, we show that incorporating these characteristics gives more awareness of the type of resources present and can enable the algorithms to manage resources more efficiently in more hybrid environments than other algorithms found in the literature.

中文翻译：

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混合露水计算环境下用于调度和分配作业的新启发式方法

由于移动和物联网设备的普遍性及其不断增长的处理潜力，移动网格计算已成为研究人员的热门话题。尽管文献中针对标准网格计算方案存在许多利用这些资源的调度算法，但令人惊讶的是，在混合动力计算资源（通常在露水和边缘计算环境中找到）的背景下，对此问题知之甚少。本文提出了一种新的算法，该算法可感知设备的电源，以便在混合环境（即电池供电的设备和非电池供电的设备协同工作）中调度任务。我们通过扩展DewSim来模拟混合露水/边缘环境，DewSim是一个模拟器，该模拟器使用从真实移动设备中提取的电池轨迹来模拟电池驱动设备的电池行为。我们比较了本文提出的算法使用以前开发的算法作为基线的吞吐量和工作完成率，该算法考虑了计算资源，但仅从依赖于电池的设备（称为增强型简单能源感知计划（E-SEAS））中进行计算。在仿真中获得的结果表明，与E-SEAS相比，我们提出的算法在混合环境中可以获得高达90％的整体吞吐量增长和约95％的已完成工作。最后，我们证明，结合这些特征可以使人们对现有资源的类型有更多的了解，并且可以使算法在更多混合环境中比文献中的其他算法更有效地管理资源。