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Stackelberg Game for Service Deployment of IoT-Enabled Applications in 6G-Aware Fog Networks

机译:Stackelberg游戏,用于服务部署IOS启用的应用程序,在6G感知的雾网络中

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摘要

Fog computing has emerged as a promising paradigm that borrows the user-oriented cloud services to the proximity of the Internet-of-Things (IoT) users in sixth-generation (6G) networks. Currently, service providers establish a proprietary fog architecture to prolong a specific group of IoT users by offering resources and services to the edge level. However, this sort of activity creates a service barrier and limits the development of fog services to the IoT-users. Keeping this in mind, we develop a 6G-aware fog federation model for utilizing maximum fog resources and providing demand specific services across the network while maximizing the revenue of fog service providers and guaranteeing the minimum service delay and price for IoT-users. To achieve this goal, we formulate our objective function into a mixed-integer nonlinear problem. By jointly optimizing the dynamic services cost and user demands, a noncooperative Stackelberg game interaction algorithm is formulated to schedule the fog and cloud resources distributively. Further maximizing the profit for the service providers and the seamless resource provisioning, a resource controller is initiated to manage the available fog resources. Extensive simulation analysis over 6G-aware Quality-of-Service parameters demonstrates the superiority of the proposed fog federation model and it reduces up to 15%-20% service delay and 20%-25% of service cost over the standalone fog and cloud frameworks.
机译:雾计算已成为一个有前途的范式,借用以第六代(6G)网络中的内部内容(物联网)用户附近的用户导向的云服务。目前,服务提供商通过向边缘级别提供资源和服务来建立专有的雾架,以延长特定的IOT用户。但是,这种活动创建了服务障碍并限制了对IOT用户的雾服务的开发。请记住这一点,我们开发了6G感知的迷人联合模型,用于利用最大雾资源,并在整个网络上提供需求特定服务,同时最大限度地提高雾服务提供商的收入,并保证IOT用户的最低服务延迟和价格。为了实现这一目标,我们将客观函数与混合整数非线性问题制定。通过联合优化动态服务成本和用户需求,配制了非支持的Stackelberg游戏交互算法以分布地调度雾和云资源。进一步最大化服务提供商和无缝资源配置的利润,启动资源控制器以管理可用的雾资源。超过6G感知质量参数的广泛仿真分析演示了拟议的FOG联合模型的优越性,并且在独立雾和云框架上减少了高达15%-20%的服务延迟和20%-25%的服务费用。

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