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Fog computing is a promising architecture to provide economic and low latencydata services for future Internet of things (IoT)-based network systems. Itrelies on a set of low-power fog nodes that are close to the end users tooffload the services originally targeting at cloud data centers. In this paper,we consider a specific fog computing network consisting of a set of dataservice operators (DSOs) each of which controls a set of fog nodes to providethe required data service to a set of data service subscribers (DSSs). How toallocate the limited computing resources of fog nodes (FNs) to all the DSSs toachieve an optimal and stable performance is an important problem. In thispaper, we propose a joint optimization framework for all FNs, DSOs and DSSs toachieve the optimal resource allocation schemes in a distributed fashion. Inthe framework, we first formulate a Stackelberg game to analyze the pricingproblem 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 resourcepurchased by the DSSs, a many-to-many matching game is applied to investigatethe pairing problem between DSOs and FNs. Finally, within the same DSO, weapply another layer of many-to-many matching between each of the paired FNs andserving DSSs to solve the FN-DSS pairing problem. Simulation results show thatour proposed framework can significantly improve the performance of theIoT-based network systems.

Computing Resource Allocation in Three-Tier IoT Fog Networks: a Joint Optimization Approach Combining Stackelberg Game and Matching