Over the years, fog computing has emerged as a paradigm to complement the cloud computing in handling the delay sensitive IoT applications in a better manner. Using fog resources, better performance such as in-time service delivery, reduced network load, optimal energy usage etc. can be achieved. With such performance gain, users availing the IoT services are more satisfied. A well-known metric Quality of Experience (QoE), used to measure the satisfaction of IoT users, can be improved by enhancing the performance of the IoT applications. Fog computing is a geographically distributed paradigm and primary service of fog computing may not include the execution of offloaded tasks/applications from the IoT devices. This makes QoE aware placement of applications in fog computing a greater challenge. Since placement algorithm is itself a computational task and both IoT applications and fog nodes need a mediator fog node to execute the placement algorithm, the placement policy should be light weighted in terms of computational complexity. This work proposes a lightweight QoE aware application placement policy in fog computing using Modified TOPSIS that prioritizes the applications and fog instances based on their expectation and computational capability respectively for the placement. Modified TOPSIS inherits all the features of classical TOPSIS while it removes rank reversal problem of classical TOPSIS. Simulation experiments, for a comparative study, depict that the proposed model not only achieves the desired resource utilization, processing time, and reduced network congestion but reduces the application placement time also significantly compared to the state of art.

多年来，雾计算已成为一种范式，可以补充云计算，从而更好地处理对延迟敏感的物联网应用。使用雾资源，可以实现更好的性能，例如及时的服务交付，减少的网络负载，最佳的能源使用等。有了这样的性能提升，使用物联网服务的用户就会更加满意。可以通过增强IoT应用程序的性能来改善用于衡量IoT用户满意度的众所周知的体验质量（QoE）指标。雾计算是一种地理分布的范式，雾计算的主要服务可能不包括从IoT设备执行卸载的任务/应用程序。这使得QoE感知的应用程序在雾计算中的位置面临更大的挑战。由于放置算法本身是计算任务，并且物联网应用程序和雾节点都需要中介者雾节点来执行放置算法，因此应根据计算复杂度对放置策略进行轻量化。这项工作提出了一种使用修改后的TOPSIS的雾计算中的轻量级QoE感知应用放置策略，该策略根据应用和雾实例分别对放置的期望和计算能力来对它们进行优先级排序。改进的TOPSIS继承了经典TOPSIS的所有功能，同时消除了经典TOPSIS的秩逆问题。为了进行比较研究，仿真实验表明，提出的模型不仅可以实现所需的资源利用率，处理时间，