- مبلغ: ۸۶,۰۰۰ تومان
- مبلغ: ۹۱,۰۰۰ تومان
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 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 fog nodes to provide the required data service to a set of data service subscribers (DSSs). How to allocate the limited computing resources of fog nodes (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 FNDSS pairing problem. Simulation results show that our proposed framework can significantly improve the performance of the IoTbased network systems.
In this paper, we proposed a joint optimization framework in the multi-FN, multi-DSO and multi-DSS scenario for IoT fog computing. In the framework, we first modeled the Stackelberg games to solve the pricing problem of the DSOs and resource purchasing problem of the DSSs. Then a many-tomany matching was proposed between the DSOs and the FNs to deal with the DSO-FN pairing problem. Finally, we applied another many-to-many matching between its paired FNs and serving DSSs to solve the FN-DSS pairing problem within the same DSO. For each stage of the problem, all participants were able to achieve the equilibrium or stable results where no one was able to change its behavior unilaterally for a higher utility. Simulation results showed that all FNs, DSOs and DCOs were able to reach optimal utilities for themselves, and high performance of the proposed framework could be achieved compared with the data services without fog nodes. For the future work, firstly the dynamic computing resource allocation problem can be considered in the three-tier IoT fog network with dynamic Stackelberg game, where each DSO is able to predict its future demands and to rent the computing resources of FNs in advance. Secondly, the analysis of cooperative and competitive behaviors among FNs may provide grouping strategies for FNs to achieve higher revenues. Correspondingly, the effective strategies are required for each DSO to prevent the severe competition for some FNs with other DSOs.