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C-RAN (Cloud Radio Access Network) is a 5G architecture that consists of sites and three-layer Data Centers (DCs), which include the central office DC, local DC, and regional DC. Network slicing, which enables infrastructure providers (InP) to create independent logical networks, is essential in this architecture. By utilizing this technology, InPs can maximize the utility of the network by providing slices to service providers in response to their slice requests. However, almost all of the recent research on slice admission control (SAC) schemes has only considered one or two layers of DCs, which limits the efficiency of the slicing process and decreases network utility. To address these issues, this paper proposes an intelligent SAC scheme called ISAC that considers all three-layer DCs. Instead of relying on reinforcement learning algorithms like Q-learning, which are effective in discrete environments with limited state space but give poor performance in continuous environments, ISAC employs the Approximate Reinforcement Learning (ARL) algorithm. ARL is better suited for 5G network modeling because it can adapt to continuous environments, allowing for a more accurate representation of the underlying physical processes. Extensive simulation studies demonstrate that ISAC significantly improves performance in terms of slice request rejection rates, InP revenue, accepting more slices, and optimizing resource utilization. [ABSTRACT FROM AUTHOR]

Big data processing, scientific calculations, and multimedia operations are some applications that require very complex time-consuming computations which cannot be performed on personal computers. Utilizing powerful cloud resources is a common method to address this problem. The amount of energy consumption of cloud data centers is an important challenge in these complex calculations, and reducing the energy consumption of cloud data centers is one of the most important goals of the researches in this area. The proposed method of this paper, called multi-agent deep Q-network with coral reefs optimization (MDQ-CR), combines the coral reefs optimization algorithm and multi-agent deep Q-network to reduce the energy consumption of data centers and cloud resources using the dynamic voltage and frequency scaling (DVFS) technique. The MDQ-CR has two main phases. The first phase exploits coral reefs optimization algorithm with a short-term view, and the second phase uses deep Q-network with a long-term view. The Markov game model is used to lead the learning agents to converge to the global optimal solution. Since processors consume the highest amount of energy of cloud compared to the other resources, the proposed method focuses on reducing the processors' energy consumption. Reducing the voltage and frequency of processors, considering expiration times of their tasks, can reduce their energy consumption significantly. The empirical experiments show that the proposed method can save energy about 89% compared to completely randomized methods, and about 20% compared to the two recent methods of the literature. [ABSTRACT FROM AUTHOR]