Network-Slice-AwareVNF Orchestration for Mission-Critical IoT in 6G Networks: A Multi-Objective Deep Reinforcement Learning Approach

Mission-critical Internet of Things (MC-IoT) applications, such as remote surgery, autonomous drone swarms, industrial safety monitoring, etc., have ultra-stringent requirements for reliability (99.999%), latency (sub-millisecond) and inter-slice isolation that require dedicated network slices with carefully orchestrated virtual network function (VNF) chains. While prior solutions to VNF orchestration seek to merge often conflicting requirements into a single objective with a scalar reward, or to statically configure the allocation of resources that fail to adjust to dynamic mission-critical workloads, both strategies are inadequate. In this paper, we propose a network slicing-aware NFV orchestration framework called SliceNFV, which uses multi-objective deep reinforcement learning (MO-DRL) to optimize the resource allocation of VNFs per slice, the inter-slice isolation enforcement, and the efficiency of the cross-slice resource sharing. It is formulated as the orchestration problem as a multi-objective constrained Markov decision process (MO-CMDP), and a novel Pareto-Conditioned Proximal Policy Optimization (PC-PPO) algorithm is developed to learn a portfolio of non-dominated orchestration policies, allowing the operator to choose operating points along the reliability–efficiency frontier without retraining. A temporal convolutional network (TCN) is a scalable network that uses a predictive slice-aware VNF scaling mechanism to forecast the arrival of mission-critical events and scale resources in advance to avoid SLA breaches. The results of extensive simulation-based assessment across three progressive scenarios—steady-state operation, mission-critical demand surge and infrastructure degradation—highlight that SliceNFV offers 99.998% reliability when operating under an SLA violation rate of 0.08%, whereas all other baselines exceed their corresponding SLAs; in addition, SliceNFV significantly improves Pareto hypervolume by 36.4% over traditional multi-objective approaches while increasing infrastructure utilization to 76%.