论文标题
基于QOE的收入最大化动态资源分配和针对雾化的关键任务物联网应用程序的定价
QoE Based Revenue Maximizing Dynamic Resource Allocation and Pricing for Fog-Enabled Mission-Critical IoT Applications
论文作者
论文摘要
雾计算已成为物联网（IoT）应用程序的重要组成部分，它充当其计算引擎。关键任务的物联网应用程序对延迟非常敏感，这取决于云服务器的物理位置。云服务提供商（CSP）可以使用各种响应率的雾气节点，并且面临将顺序接收到的物联网数据转发到处理之一的FOG节点的挑战。由于请求的到达时间和性质是随机的，因此在FOG节点上实时对请求进行最佳分类并分配可用的虚拟机实例（VMI）很重要，以便为用户提供高QOE，并为CSP产生更高的收入。在本文中，我们根据分配的应用程序使用了基于应用程序的QoE的定价策略，并根据计算请求的统计信息获得了最佳的动态分配规则。与文献中其他静态匹配方案相比，开发的解决方案在统计上是最佳，动态和可实现的。已经使用模拟评估了所提出的框架的性能，与基准方案相比，结果显示出显着改善。
Fog computing is becoming a vital component for Internet of things (IoT) applications, acting as its computational engine. Mission-critical IoT applications are highly sensitive to latency, which depends on the physical location of the cloud server. Fog nodes of varying response rates are available to the cloud service provider (CSP) and it is faced with a challenge of forwarding the sequentially received IoT data to one of the fog nodes for processing. Since the arrival times and nature of requests is random, it is important to optimally classify the requests in real-time and allocate available virtual machine instances (VMIs) at the fog nodes to provide a high QoE to the users and consequently generate higher revenues for the CSP. In this paper, we use a pricing policy based on the QoE of the applications as a result of the allocation and obtain an optimal dynamic allocation rule based on the statistical information of the computational requests. The developed solution is statistically optimal, dynamic, and implementable in real-time as opposed to other static matching schemes in the literature. The performance of the proposed framework has been evaluated using simulations and the results show significant improvement as compared with benchmark schemes.
