Fog computing is an emerging computation technology for handling and processing the data from IoT devices. The devices such as the router, smart gateways, or micro-data centers are used as the fog nodes to host and service the IoT applications. However, the primary challenge in fog computing is to find the suitable nodes to deploy and run the IoT application services as these devices are geographically distributed and have limited computational resources. In this paper, we design the two-level resource provisioning fog framework using docker and containers and formulate the service placement problem in fog computing environment as a multi-objective optimization problem for minimizing the service time, cost, energy consumption and thus ensuring the QoS of IoT applications. We solved the said multi-objective problem using the Elitism-based Genetic Algorithm (EGA). The proposed approach is evaluated on fog computing testbed developed using docker and containers on 1.4 GHz 64-bit quad-core processor devices. The experimental results demonstrate that the proposed method outperforms other state-of-the-art service placement strategies considered for performance evaluation in terms of service cost, energy consumption, and service time.

雾计算是一种新兴的计算技术，用于处理和处理来自IoT设备的数据。诸如路由器，智能网关或微数据中心之类的设备被用作雾节点来托管和服务于物联网应用。但是，雾计算的主要挑战是找到合适的节点来部署和运行IoT应用程序服务，因为这些设备在地理位置上分布并且计算资源有限。在本文中，我们设计了使用docker和容器的两级资源供应雾框架，并将雾计算环境中的服务放置问题表述为多目标优化问题，以最大程度地减少服务时间，成本，能耗，从而确保QoS。物联网应用程序。我们使用基于精英的遗传算法（EGA）解决了上述多目标问题。在1.4 GHz 64位四核处理器设备上使用docker和容器开发的雾计算测试平台上对提出的方法进行了评估。实验结果表明，该方法在服务成本，能耗和服务时间方面均优于其他用于性能评估的最新服务放置策略。