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DSO: An Intelligent SFC Orchestrator for Time and Resource Intensive Ultra Dense IoT Networks

Among the massive pool of Internet of Things (IoT) devices in network function virtualization (NFV) context, the urgency for efficient service orchestration is constantly growing. The emerging challenges can be addressed as collaborative optimization of resource utilities and ensuring Quality-of-Service (QoS) with prompt orchestration in dynamic, congested, and resource-hungry IoT networks. Traditional mathematical programming models are NP-hard, hence inappropriate for time sensitive IoT scenarios. This paper promotes the need to go beyond the realms and propose an intelligent Deep Q-Network (DQN) driven service function chain (SFC) orchestration, named as DSO hereafter. We further equip this proposed DSO model with the notion of sharing the flow of already deployed network function rather than urging a new instantiation. The sharing conceptualization improves resource utilization, and DQN is employed for adaptive, robust, and swift orchestration. Our extensive simulation results demonstrate the remarkable capability and adaptability of the proposed DSO model for cutting back running time (≈ 10 hours) and ensuring near-optimal resource utilization across extremely dense IoT substrate network settings. Thus, this research can be regarded as a pioneering tread to scale down massive IoT resource fabrication costs, upgrade profit margin for providers, and sustain QoS.