Fog computing has been proposed as an emerging approach to the evolution of the cloud computing platform and the expansion of the IoT to the edge of the network. In this type of computing, service providers can control signals by assigning specific tasks to users, and tasks sensitive to offload users’ latency are transferred to distributed fog nodes (FNs) at the edge of the network. In this article, with the aim of optimizing system performance and user satisfaction, we will examine the issue of allocating joint radio and computing resources. In this paper, evaluate the impact of each of the cache parameters, CPU clock frequency performance, bandwidth, CPU cycle, aging and IPC (Instruction per cycle/clock) to Iot-Fog users, to provide a model for allocating computational and radio resources in Iot computing. This algorithm performs the process of selecting the optimal resources for data processing in the fog layer, taking into account both energy and latency criteria at the same time. In this paper, a matching game framework, called Student Project Allocation (SPA), has been used to distribute solutions to the issue of radio and computational resource allocation, instead of the usual centralized optimization. An efficient SPA- (S, P) algorithm has been implemented to solve the problem and the stable SPA results. In addition, external effects instability, or, in other words, independence between identical players, is eliminated by the proposed User-oriented cooperation strategy (UOC). The results showed that our proposed framework can provide a distributed and close to optimal performance both from the perspective of users and from the perspective of the system. Therefore, it can take advantage of the combined approach of SPA- (S, P) and UOC in order to allocate joint radio and computational resource allocation in fog computing for IoT environment. Also, the proposed method of this research can be used to allocate joint radio and computational resources in limited resource devices, delayed sensitive programs and conditions of bandwidth limitation in various types of fog computing in IoT environment.

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