# ORIENT: A Priority-Aware Energy-Efficient Approach for Latency-Sensitive Applications in 6G

arXiv: https://arxiv.org/abs/2402.06931 · HTML: https://mlsanity.com/paper/2402.06931/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 16 references that are in the map.
- First posted on arXiv 10 Feb 2024.

## Builds on (most cited first, 5 of 5)

- [Dueling Network Architectures for Deep Reinforcement Learning](https://mlsanity.com/paper/1511.06581/) (2015)
- [A Semantic-Aware Multiple Access Scheme for Distributed, Dynamic 6G-Based Applications](https://mlsanity.com/paper/2401.06308/) (2024)
- [Double Deep Q-Learning-based Path Selection and Service Placement for Latency-Sensitive Beyond 5G Applications](https://mlsanity.com/paper/2309.10180/) (2023)
- [QoS-Aware Service Prediction and Orchestration in Cloud-Network Integrated Beyond 5G](https://mlsanity.com/paper/2309.10185/) (2023)
- [Joint Network Slicing, Routing, and In-Network Computing for Energy-Efficient 6G](https://mlsanity.com/paper/2401.06306/) (2024)

## Built on by (most cited first, 1 of 1)

- [Fairness-Utilization Trade-off in Wireless Networks with Explainable Kolmogorov-Arnold Networks](https://mlsanity.com/paper/2411.01924/) (2024)

## Abstract (arXiv)

Anticipation for 6G's arrival comes with growing concerns about increased energy consumption in computing and networking. The expected surge in connected devices and resource-demanding applications presents unprecedented challenges for energy resources. While sustainable resource allocation strategies have been discussed in the past, these efforts have primarily focused on single-domain orchestration or ignored the unique requirements posed by 6G. To address this gap, we investigate the joint problem of service instance placement and assignment, path selection, and request prioritization, dubbed PIRA. The objective function is to maximize the system's overall profit as a function of the number of concurrently supported requests while simultaneously minimizing energy consumption over an extended period of time. In addition, end-to-end latency requirements and resource capacity constraints are considered for computing and networking resources, where queuing theory is utilized to estimate the Age of Information (AoI) for requests. After formulating the problem in a non-linear fashion, we prove its NP-hardness and propose a method, denoted ORIENT. This method is based on the Double Dueling Deep Q-Learning (D3QL) mechanism and leverages Graph Neural Networks (GNNs) for state encoding. Extensive numerical simulations demonstrate that ORIENT yields near-optimal solutions for varying system sizes and request counts.