## 4 DQN-Based Priority/Demanding Resource Management
### DQN-Based Solutions
The cost sub-reward is crucial as it focuses on reducing the financial burden of industrial service deployments, including minimizing resource provisioning costs, network bandwidth usage, and overall infrastructure expenditures. DQG-PD ensures efficient management of each prioritized slice, thereby lowering operational costs while maintaining the required QoS and QoE. The energy sub-reward focuses on reducing the consumption amount that the service operates and network traffic experiences while traversing a complete performance. The DQG-PD algorithm optimizes the resource placement and routing decisions to reduce end-to-end latency with prioritized demand considered on energy metrics, improving overall service responsiveness and green computation. DQG-PD achieved 28.98 scores, which accounted for 29.34% of the total reward metrics, emphasizing the energy-focused and bettering performances compared to MT-HRT-PD (9.13 higher) and SADRL-PD (19.85 higher) in Figure 5.

Finally, the sub-reward on reliability metrics, as shown in Figure 6, emphasizes the algorithm's robustness and fault tolerance. DQG-PD achieved a high-reliability score (22.68), significantly outperforming MT-HRT-PD (by 2.58) and SADRL-PD (by 4.66). This reliability is achieved by incorporating dynamic service configurations, which adapt to network failures, traffic surges, demanding congestion, or disruptions. DQG-PD ensures high availability by considering factors such as backup paths, failover mechanisms, and redundancy, thereby enhancing service reliability and minimizing downtime due to network issues. Figure 4: Sub-reward on cost. Figure 3: Sub-reward on resource. To deeply evaluate the efficiencies in controlling different demanding factors and priority levels, we captured the completion ratios of combined service requests in different topology sizes and the number of virtual chains (3, 6, 9, and 12). First, our simulation estimated the acceptance ratios when the service is requested. After accepting the requests, we executed the services through the chain to detect any possible failure. If failure is detected, we configure the restoration properties to revive the service execution. However, the properties of slice criticality vary. If restoration is successful, we capture our results as completion ratios. Over 1000 time slots, we configured the traffic rates to stimulate

Figure 5: Sub-reward on energy. Figure 6: Sub-reward on reliability. the control policies. DQG-PD achieved 99.87%, which is 10.78% and 19.54% higher than MT-HRT-PD and SADRL-PD, respectively, as shown in Figure 7. The elaboration of each phase can be discussed as follows:

* The high acceptance ratio demonstrates the controller's scalability and ability to effectively accommodate a larger number of service requests. * The restoration ratio measures how well the system recovers from service failures, ensuring uninterrupted service and high availability, particularly for high incoming task requests. * In total, we concluded the performance into completion ratios, which demonstrates its effectiveness in completing tasks even under heavy loads. ## 6 Conclusions

This paper presented priority/demanding-based resource management to address optimal resource consumption and energy, which enabled O-RAN to aim at a cognitive IIoT network. The proposed scheme leveraged MEC-incorporated O-RAN to handle resource efficiency and resource management. DQG-PD leverages virtualization and DQN agents to direct resource allocation to industrial services according to priority demand. DQG-PD framework ensures sufficient system components for automated policy orchestration, including resource management, energy, cost, and service priorities. In this framework, DQG-PD enhances resource management and energy efficiency for resource-constrained IIoT devices and scales into a system for heterogeneity critical in real-time. Our results demonstrate that the DQG-PD algorithm significantly enhances computation, resource utilization, and energy efficiency when compared to existing reference schemes.