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With the increasing growth of information through smart devices, increasingthe quality level of human life requires various computational paradigmspresentation including the Internet of Things, fog, and cloud. Between thesethree paradigms, the cloud computing paradigm as an emerging technology addscloud layer services to the edge of the network so that resource allocationoperations occur close to the end-user to reduce resource processing time andnetwork traffic overhead. Hence, the resource allocation problem for itsproviders in terms of presenting a suitable platform, by using computationalparadigms is considered a challenge. In general, resource allocation approachesare divided into two methods, including auction-based methods(goal, increaseprofits for service providers-increase user satisfaction and usability) andoptimization-based methods(energy, cost, network exploitation, Runtime,reduction of time delay). In this paper, according to the latest scientificachievements, a comprehensive literature study (CLS) on artificial intelligencemethods based on resource allocation optimization without consideringauction-based methods in various computing environments are provided such ascloud computing, Vehicular Fog Computing, wireless, IoT, vehicular networks, 5Gnetworks, vehicular cloud architecture,machine-to-machinecommunication(M2M),Train-to-Train(T2T) communication network, Peer-to-Peer(P2P)network. Since deep learning methods based on artificial intelligence are usedas the most important methods in resource allocation problems; Therefore, inthis paper, resource allocation approaches based on deep learning are also usedin the mentioned computational environments such as deep reinforcementlearning, Q-learning technique, reinforcement learning, online learning, andalso Classical learning methods such as Bayesian learning, Cummins clustering,Markov decision process.

The state-of-the-art review on resource allocation problem using artificial intelligence methods on various computing paradigms