Due to the growing popularity of the Internet of Things, edge computing concept has been widely studied to relieve the load on the original cloud and networks while improving the service quality for end-users. To simulate such a complex environment involving edge and cloud computing, EdgeCloudSim has been widely adopted. However, it suffers from certain efficiency and scalability issues due to the ignorance of the deficiency in the originally adopted data structures and maintenance strategies. Specifically, it generates all events at beginning of the simulation and stores unnecessary historical information, both result in unnecessarily high complexity for search operations. In this work, by fixing the mismatches on the concept of discrete-event simulation, we propose enhancement of EdgeCloudSim which improves not only the runtime efficiency of simulation, but also the flexibility and scalability. Through extensive experiments with statistical methods, we show that the enhancement does not affect the expressiveness of simulations while obtaining 2 orders of magnitude speedup, especially when the device count is large.
翻译:由于Things互联网越来越受欢迎,对边缘计算概念进行了广泛研究,以减轻原始云层和网络的负担,同时提高终端用户的服务质量。为模拟涉及边缘和云计算等复杂环境,EdgeCloudSim被广泛采用。然而,由于对最初采用的数据结构和维护战略的缺陷认识不足,EdgeCloudSim受到某些效率和可缩放性问题的影响。具体地说,它在模拟开始时产生所有事件,储存不必要的历史信息,造成搜索操作不必要的高度复杂。在这项工作中,我们建议通过解决离散事件模拟概念的不匹配,加强EdgeCloudSim,不仅改进模拟的运行时间效率,而且改进灵活性和可缩放性。通过广泛的统计方法实验,我们表明这种增强不会影响模拟的外观性,同时获得两个级的加速速度,特别是当装置数量巨大时。