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

• URL: http://arxiv.org/abs/2402.06931v1
• Date: Sat, 10 Feb 2024 12:05:52 GMT
• Title: ORIENT: A Priority-Aware Energy-Efficient Approach for Latency-Sensitive Applications in 6G
• Authors: Masoud Shokrnezhad and Tarik Taleb
• Abstract summary: 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. We investigate the joint problem of service instance placement and assignment, path selection, and request prioritization, dubbed PIRA.
• Score: 15.753216159980434
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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