Reinforcement Learning-based Load Controller in IP Multimedia Subsystems

kground and Objectives: IP multimedia subsystems (IMS) have been introduced as the Next Generation Network (NGN) platform while considering Session Initiation Protocol (SIP) as the signaling protocol. SIP lacks a proper overload mechanism. Hence, this challenge causes decline in the multimedia QoS. The main propose of overload control mechanism is to keep the network throughput at the same network capacity with overload.Methods: NGN distributed with IMS is a complex innovative network consisting of interacting subsystems. Hence, multi-agent systems (MAS) receiving further attention for solving complex problems can solve the problem of overload in these networks. To this end, each IMS server is considered as an intelligent agent that can learn and negotiate with other agents while maintaining autonomy, thus eliminating the overload by communication and knowledge transfer between the agents. In the present research, using MAS and their properties, the intelligent hop by hop method is provided based on Q-learning and negotiation capability for the first time.Results: In the proposed method, parameters of overload controller are obtained by reinforcement learning. In order to check the validity of controller performance, a comparison is made with the similar method in which the optimal parameters are achieved based on trial and error. The result of the comparison confirms the validity of the proposed method. In order to evaluate the efficiency of the learner method, it is compared with similar and standard methods, for which the results are compared to show performance. The results show, the proposed method has approximately improved the throughput by ۱۳%, the delay by ۴۹% and the number of rejected sessions by ۱۷% compare with methods, passing control messages through the network such as CPU occupancy methods. While compare with external controller methods like holonic, throughput is improved by ۱% and the number of rejected requests is decreased by ۱۰%, but delay is increased by ۶% due to the convergence time of the learning and negotiation process.Conclusion: To overcome overload, complex IMS servers are considered as learner and negotiator agents. This is a new method to achieve the required parameters without relying on expert knowledge or person as well as, heterogeneous IMS entities can be inserted into the problem to complete study in future.

Keywords:

IMS , SIP , Overload , Multi-agent system , Reinforcement learning