An Effective Deep Q-Learning Architecture for Class Mapping in Multi-domain Software-Defined Internet of Things - Ali, Jehad; Chandroth, Jisi; Roh, Byeong Hee (Lecture Notes in Networks and Systems, 2025)
There are specific communication requirements for each Internet of Things (IoT) application, such as jitter, packet delivery ratio (PLR), and latency. In heterogeneous networks, an end-to-end (E2E) route may pass across several domains with various quality-of-service (QoS) traffic classes in each domain, making it difficult to meet these unique E2E QoS criteria when they are present on the E2E path of IoT networks. This paper provides a hierarchical software-defined networking (SDN) architecture employing deep Q-learning and a multi-criteria decision-making (MCDM) scheme to determine the appropriate QoS class for the E2E route in the SD-IoT heterogeneous network and performs its mapping for the E2E service requests. The architecture is composed of two layers. In the global controller, we map the appropriate service classes for supplying E2E QoS in accordance with application service requests. Our proposed framework is demonstrated with different real Internet topologies.
Keywords: Class mapping; Deep Q-learning; Learning architectures; Multi-domains; Packet delivery ratio; Q-learning; Quality-of-service; Service class; Service requests; Software-defined networkings; Class mapping; Deep Q-learning; Internet of Things; QoS; Software-defined networking