arXiv subjects

Congzhou Li

Publications and source records attributed to Congzhou Li.

4 recordsLinked to original sources

arXiv2026

RL-based Network Slice Embedding over Space Division Multiplexed Elastic Optical Networks

Network slicing over space-division-multiplexed elastic optical networks (SDM-EONs) requires jointly managing spectrum, spatial cores, and compute resources, a coupling that many existing studies ignore by treating compute placement independently from routing and spectrum decisions. This disconnect can cause the spectrum to be allocated along a path, only for the request to fail due to insufficient compute resources along the path, or may result in compute resources being allocated without consideration for spectrum resource availability on the path between compute nodes. We propose a path-constrained reinforcement learning framework that addresses compute node selection and RMCSA, being aware of both resources, restricting the RL agent's action space to nodes along $k$-shortest paths between request endpoints. Training incorporates reward shaping to improve robustness under high load. We propose PPO-Full (Proximal Policy Optimization-Full), which jointly selects compute nodes and routing paths via a multi-dimensional action space, against distance-based heuristics, a greedy baseline, and a decoupled VONE-DRL baseline on a 24-node USNET topology under hotspot traffic conditions. Results demonstrate consistent improvements in acceptance rate over all baselines at high load, with gains becoming more pronounced as traffic intensity increases.

cs.NI↗

arXiv2026

Network Slice Embedding over Space Division Multiplexed Elastic Optical Networks

Network slicing over space division multiplexed elastic optical networks (SDM EONs) enables efficient multiservice provisioning on a shared optical substrate. However, embedding such slices requires coordinated spectrum and compute resource management under dynamic traffic, which most existing RMCSA studies treat independently. This paper focuses on the network slice embedding problem over space division multiplexed elastic optical networks (SDM EONs), aiming to develop efficient resource allocation strategies that ensure both high utilization and reliable service performance. While prior studies have investigated routing, modulation format, core, and spectrum allocation (RMCSA), they typically consider these dimensions separately from compute placement. To address this gap, this paper proposes a Waypoint Assisted Multi Segment Slice Mapping (WMSM) scheme, which integrates compute placement with spectrum allocation in a sequential but coupled manner to enable more flexible resource placement and improved spectrum efficiency. Numerical results show that WMSM improves acceptance ratios by up to 27% under high load conditions, while achieving up to 47% lower total provisioning cost relative to the baseline strategy. These results highlight the benefits of integrated compute spectrum provisioning and provide design insights for scalable, compute aware optical slice mapping.

cs.NI↗

arXiv2024

A Multi-Agent Reinforcement Learning Scheme for SFC Placement in Edge Computing Networks

In the 5G era and beyond, it is favorable to deploy latency-sensitive and reliability-aware services on edge computing networks in which the computing and network resources are more limited compared to cloud and core networks but can respond more promptly. These services can be composed as Service Function Chains (SFCs) which consist of a sequence of ordered Virtual Network Functions (VNFs). To achieve efficient edge resources allocation for SFC requests and optimal profit for edge service providers, we formulate the SFC placement problem in an edge environment and propose a multi-agent Reinforcement Learning (RL) scheme to address the problem. The proposed scheme employs a set of RL agents to collaboratively make SFC placement decisions, such as path selection, VNF configuration, and VNF deployment. Simulation results show our model can improve the profit of edge service providers by 12\% compared with a heuristic solution.

cs.NI↗

arXiv2024

Multi-domain Network Slice Partitioning: A Graph Neural Network Algorithm

In the context of multi-domain network slices, multiple domains need to work together to provide a service. The problem of determining which part of the service fits within which domain is referred to as slice partitioning. The partitioning of multi-domain network slices poses a challenging problem, particularly when striving to strike the right balance between inter-domain and intra-domain costs, as well as ensuring optimal load distribution within each domain. To approach the optimal partition solution while maintaining load balance between domains, a framework has been proposed. This framework not only generates partition plans with various characteristics but also employs a Graph Neural Network solver, which significantly reduces the plan generation time. The proposed approach is promising in generating partition plans for multi-domain network slices and is expected to improve the overall performance of the network.

cs.NI↗