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Deployment of Multi-tier Fog Computing System for IoT Services in Smart City

机译:在智慧城市中部署用于物联网服务的多层雾计算系统

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One of the assuring technologies today is Fog computing, focus on the extensive use of the computational and storage capacities of the end devices in a network. In present day there due to the enormous amount of data from the number of IoT devices, a centralized cloud system is quite inadequate. This challenge can be addressed by deploying Fog devices neighbouring to these IoT devices so as to provide with real-time response. Thus for the creation of a smart city we advance towards an architecture in which the cloud data centre at the top followed by SDN controllers, fog controllers, fog devices and smart sensors. We propose an integer programming model in our problem formulation of deploying the fog nodes, fog controllers, SDN controllers which results in minimization of latency, traffic and cost with constraints such as device capacity, offloading workload, range etc. Further on, our work solve this NP-hard problem by weighted sum method and the two meta-heuristic algorithms Genetic Algorithm (GA), Particle Swarm Optimization (PSO) compared to Randomized Algorithm. Thus a network planner with a cost efficient fog network in mind can relate to the simulations illustrated in the paper for their existing computational and storage configuration. We verify our proposed model and algorithms through simulation which help to design efficient fog network.
机译:当今的保证技术之一是雾计算,它专注于网络中终端设备的计算和存储容量的广泛使用。如今,由于来自大量IoT设备的大量数据,集中式云系统非常不足。可以通过在这些IoT设备附近部署Fog设备来解决此难题,以提供实时响应。因此,为了创建智能城市,我们朝着一种架构发展,在该架构中,顶部的云数据中心紧随其后的是SDN控制器,雾控制器,雾设备和智能传感器。在部署雾节点,雾控制器,SDN控制器的问题公式中,我们提出了整数规划模型,该模型可最大程度地减少延迟,流量和成本,并限制设备容量,卸载工作量,范围等。该NP难问题通过加权求和法与两种元启发式算法遗传算法(GA),粒子群优化(PSO)相比,采用了随机算法。因此,考虑到具有成本效益的雾网络的网络规划人员可以将其现有的计算和存储配置与本文中说明的仿真相关。我们通过仿真验证了我们提出的模型和算法,这有助于设计有效的雾网。

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