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Deadline-based dynamic resource allocation and provisioning algorithms in Fog-Cloud environment

机译:雾云环境中基于截止日期的动态资源分配和供应算法

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摘要

The Fog computing paradigm is becoming prominent in supporting time-sensitive applications that are related to the smart Internet of Things (IoT) services, such as smart city and smart healthcare. Although Cloud computing is a promising paradigm for IoT in data processing, due to the high latency limitation of the Cloud, it is unable to satisfy the requirements for time-sensitive applications. Resource allocation and provisioning in the Fog-Cloud environment, considering dynamic changes in user requirements and limited available resources in Fog devices, is a challenging task. Among dynamic changes in the parameters of user requirements, the deadline is the most important challenge in the Fog computing environment. Current works on Fog computing address the resource provisioning without considering the dynamic changes in users' requirements. To address the problem of satisfying deadline-based dynamic user requirements, we propose resource allocation and provisioning algorithms by using resource ranking and provision of resources in a hybrid and hierarchical fashion. The proposed algorithms are evaluated in a simulation environment by extending the CloudSim toolkit to simulate a realistic Fog environment. The experimental results indicate that the performance of the proposed algorithms is better compared with existing algorithms in terms of overall data processing time, instance cost and network delay, with the increasing number of application submissions. The average processing time and cost are decreased by 12% and 15% respectively, compared with existing solutions.
机译:在支持与时间相关的与智能物联网(IoT)服务相关的应用程序(例如智能城市和智能医疗保健)时,雾计算范例正变得越来越重要。尽管云计算是物联网在数据处理中的一种有希望的范例,但是由于云的高延迟限制,它无法满足对时间敏感的应用程序的要求。考虑到用户需求的动态变化和Fog设备中有限的可用资源,在Fog-Cloud环境中进行资源分配和配置是一项艰巨的任务。在用户需求参数的动态变化中,截止日期是Fog计算环境中最重要的挑战。当前关于雾计算的工作解决了资源供应问题,而没有考虑用户需求的动态变化。为了解决满足基于截止时间的动态用户需求的问题,我们提出了一种资源分配和供应算法,该算法通过使用资源等级和资源的混合和分层方式来提供资源。通过扩展CloudSim工具箱以仿真现实的Fog环境,可以在仿真环境中评估提出的算法。实验结果表明,随着应用程序提交数量的增加,在总体数据处理时间,实例成本和网络延迟方面,所提出算法的性能优于现有算法。与现有解决方案相比,平均处理时间和成本分别减少了12%和15%。

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