Computing Resource Allocation in Three-Tier IoT Fog Networks: A Joint Optimization Approach Combining Stackelberg Game and Matching
Computing Resource Allocation in Three-Tier IoT Fog Networks: A Joint Optimization Approach Combining Stackelberg Game and Matching
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DOI：
10.1109/jiot.2017.2688925
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发表时间：
2017-10-01
影响因子：
10.6
通讯作者：
Han, Zhu
中科院分区：
文献类型：
--
作者：
Zhang, Huaqing;Xiao, Yong;Han, Zhu
雾计算是一种有前途的体系结构，可为未来的物联网（IoT）网络系统提供经济和低的延迟数据服务。雾计算依赖于一组低功率雾节点（FNS），这些低功耗节点（FNS）位于最终用户附近，以卸载最初针对云数据中心的服务。在本文中，我们考虑了一个特定的雾计算网络，该网络由一组数据服务运营商（DSO）组成，每个数据服务运算符（DSO）控制一组FNS，以向一组数据服务订户（DSSS）提供所需的数据服务。如何将FNS的有限计算资源分配给所有DSSs以实现最佳和稳定的性能是一个重要的问题。因此，我们为所有FN，DSO和DSSS提出了一个联合优化框架，以以分布式方式实现最佳资源分配方案。在框架中，我们首先制定了一个Stackelberg游戏，以分析DSO的定价问题以及DSSS的资源分配问题。在DSO可以知道DSSS购买的预期资源数量的情况下，应用了多对多的匹配游戏来研究DSO和FNS之间的配对问题。最后，在同一DSO中，我们在每个配对的FNS和服务DSS之间应用了另一层多到许多匹配，以解决FN-DSS配对问题。仿真结果表明，我们提出的框架可以显着改善基于IoT的网络系统的性能。
Fog computing is a promising architecture to provide economical and low latency data services for future Internet of Things (IoT)-based network systems. Fog computing relies on a set of low-power fog nodes (FNs) that are located close to the end users to offload the services originally targeting at cloud data centers. In this paper, we consider a specific fog computing network consisting of a set of data service operators (DSOs) each of which controls a set of FNs to provide the required data service to a set of data service subscribers (DSSs). How to allocate the limited computing resources of FNs to all the DSSs to achieve an optimal and stable performance is an important problem. Therefore, we propose a joint optimization framework for all FNs, DSOs, and DSSs to achieve the optimal resource allocation schemes in a distributed fashion. In the framework, we first formulate a Stackelberg game to analyze the pricing problem for the DSOs as well as the resource allocation problem for the DSSs. Under the scenarios that the DSOs can know the expected amount of resource purchased by the DSSs, a many-to-many matching game is applied to investigate the pairing problem between DSOs and FNs. Finally, within the same DSO, we apply another layer of many-to-many matching between each of the paired FNs and serving DSSs to solve the FN-DSS pairing problem. Simulation results show that our proposed framework can significantly improve the performance of the IoT-based network systems.