R-Learning Based Admission Control for Service Federation in Multi-domain 5G Networks

TL;DR

This paper develops an R-Learning based admission control method for multi-domain 5G network service federation, optimizing long-term profit without prior demand knowledge, and demonstrates its effectiveness through extensive simulations.

Contribution

It introduces a novel R-Learning approach for admission control in multi-domain 5G networks, improving upon traditional methods and providing near-optimal performance without demand rate knowledge.

Findings

R-Learning achieves at most 3-5% optimality gap.

Learning algorithms outperform greedy policies.

Performance depends on the discount factor in Q-Learning.

Abstract

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.