Service federation in 5G/B5G networks enables service providers to orchestrate network services across multiple domains where admission control is a key issue. For each demand, without knowing the future ones, the admission controller either determines the domain to deploy the demand or rejects it in order to maximize the long-term average profit. In this paper, at first, under the assumption of knowing the arrival and departure rates of demands, we obtain the optimal admission control policy by formulating the problem as a Markov decision process that is solved by the policy iteration method. As a practical solution, where the rates are not known, we apply the Q-Learning and R-Learning algorithms to approximate the optimal policy. The extensive simulation results show the learning approaches outperform the greedy policy, and while the performance of Q-Learning depends on the discount factor, the optimality gap of the R-Learning algorithm is at most 3-5% independent of the system configuration.

中文翻译：

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基于R学习的多域5G网络中服务联合的准入控制

5G / B5G网络中的服务联盟使服务提供商能够跨多个域协调网络服务，在这些域中，准入控制是一个关键问题。对于每个需求，在不知道未来需求的情况下，准入控制器要么确定要部署需求的域，要么拒绝它，以使长期平均利润最大化。在本文中，首先，在知道需求的到达和离开速度的假设下，我们通过将问题表述为由策略迭代方法解决的马尔可夫决策过程来获得最优准入控制策略。作为一种不知道费率的实用解决方案，我们应用Q学习和R学习算法来近似最佳策略。广泛的仿真结果表明，学习方法的性能优于贪婪策略，