Computing resource allocation in three-tier IoT fog networks: a joint optimization approach combining Stackelberg game and matching

摘要

Fog computing is a promising architecture to\udprovide economical and low latency data services for future\udInternet of Things (IoT)-based network systems. Fog computing\udrelies on a set of low-power fog nodes (FNs) that are located\udclose to the end users to offload the services originally targeting\udat cloud data centers. In this paper, we consider a specific\udfog computing network consisting of a set of data service operators\ud(DSOs) each of which controls a set of FNs to provide the\udrequired data service to a set of data service subscribers (DSSs).\udHow to allocate the limited computing resources of FNs to all\udthe DSSs to achieve an optimal and stable performance is an\udimportant problem. Therefore, we propose a joint optimization\udframework for all FNs, DSOs, and DSSs to achieve the optimal\udresource allocation schemes in a distributed fashion. In the\udframework, we first formulate a Stackelberg game to analyze\udthe pricing problem for the DSOs as well as the resource allocation\udproblem for the DSSs. Under the scenarios that the DSOs\udcan know the expected amount of resource purchased by the\udDSSs, a many-to-many matching game is applied to investigate\udthe pairing problem between DSOs and FNs. Finally, within the\udsame DSO, we apply another layer of many-to-many matching\udbetween each of the paired FNs and serving DSSs to solve\udthe FN-DSS pairing problem. Simulation results show that our\udproposed framework can significantly improve the performance\udof the IoT-based network systems.