Recently, to deliver services directly to the network edge, fog computing, an emerging and developing technology, acts as a layer between the cloud and the IoT worlds. The cloud or fog computing nodes could be selected by IoTs applications to meet their resource needs. Due to the scarce resources of fog devices that are available, as well as the need to meet user demands for low latency and quick reaction times, resource allocation in the fog-cloud environment becomes a difficult problem. In this problem, the load balancing between several fog devices is the most important element in achieving resource efficiency and preventing overload on fog devices. In this paper, a new adaptive resource allocation technique for load balancing in a fog-cloud environment is proposed. The proposed technique ranks each fog device using hybrid multi-criteria decision-making approaches Fuzzy Analytic Hierarchy Process (FAHP) and Fuzzy Technique for Order Performance by Similarity to Ideal Solution (FTOPSIS), then selects the most effective fog device based on the resulting ranking set. The simulation results show that the proposed technique outperforms existing techniques in terms of load balancing, response time, resource utilization, and energy consumption. The proposed technique decreases the number of fog nodes by 11%, load balancing variance by 69% and increases resource utilization to 90% which is comparatively higher than the comparable methods.

翻译：摘要：近年来，为直接将服务交付至网络边缘，雾计算作为一种新兴且不断发展中的技术，充当了云与物联网世界之间的中间层。物联网应用可根据其资源需求选择云或雾计算节点。由于可用雾设备资源稀缺，且需满足用户对低延迟和快速响应的要求，雾-云环境中的资源分配成为一项难题。在此问题中，多个雾设备间的负载均衡是实现资源效率并防止雾设备过载的最关键要素。本文提出了一种面向雾-云环境中负载均衡的新型自适应资源分配技术。该技术采用混合多准则决策方法——模糊层次分析法（Fuzzy Analytic Hierarchy Process, FAHP）与模糊理想解相似度排序法（Fuzzy Technique for Order Performance by Similarity to Ideal Solution, FTOPSIS）对每个雾设备进行排序，并根据排序结果集选择最有效的雾设备。仿真结果表明，所提技术在负载均衡、响应时间、资源利用率和能耗方面优于现有方法。该技术使雾节点数量减少11%，负载均衡方差降低69%，资源利用率提升至90%，显著高于对比方法。