Priority-Aware Resource Management for Adaptive Service Function Chaining in Real-Time Intelligent IoT Services

The growth of the Internet Things (IoT) in various mission-critical applications generates service heterogeneity with different priority labels. A set virtual network function (VNF) orders represents chaining (SFC) for a particular to robustly execute virtualization (NFV)-enabled environment. In IoT networks, configuration adaptive SFC has emerged ensure optimality and elasticity resource expenditure. this paper, priority-aware management is provided by modeling real-time requests. problem models primary features that impact optimization times utilization are studied. proposed approaches query promising embedded deep reinforcement learning engine layer (e.g., orchestrator) observe state VNFs, apply action on instantiating modifying new/created evaluate average transmission delays end-to-end services. procedures, agent formulates approximator scoring existing chain performance metrics. testbed simulation was conducted SDN/NFV topologies captured rewards, delays, delivery ratio, throughput as −48.6666, 10.9766 ms, 99.9221%, 615.8441 Mbps, which outperformed other reference approaches, following parameter