Jul 2026· International Conference on Optical Communications and Networks· pp. 1-3· 0 citations· 14 references
Abstract
This paper proposes a multi-objective Integer Linear Programming (ILP) formulation for optimal virtual Content Delivery Network (vCDN) placement in fixed broadband networks. The proposed framework jointly minimizes backhaul traffic and end-to-end latency across a six-tier topology spanning OLT, Tier 2/Tier 1 aggregation, Provider Edge, Transport Backbone, and International Gateway nodes. Key contributions include a partial caching model with ratio α***(0,1] that increases cache diversity by 1/α (Theorem 1), a view-time threshold mechanism with effective utilization factor ηⱼ, and a placement efficiency metric Φ that quantifies performance gain per unit deployment cost. Proof-of-concept simulation confirms 81.2% average RTT reduction (5.704 ms → 1.075 ms), directly validating the latency component of the ILP objective.
The rapid evolution of beyond-5G and emerging 6G networks is driving the need for flexible, reliable, and cost-efficient virtualized Radio Access Network (vRAN) architectures capable of supporting heterogeneous services such as enhanced Mobile Broadband (eMBB), Ultra-Reliable Low-Latency Communication (URLLC), and Massive Machine-Type Communication (mMTC). Future disaggregated RAN systems are expected to rely heavily on network slicing, functional split flexibility, and optical x-haul infrastructures to support stringent performance, scalability, and availability requirements. In this paper, we present an integrated framework for reliable, slice-aware, and functional split-aware Virtual Network Function (VNF) placement with lightpath provisioning in disaggregated vRAN environments. The proposed approach maximizes mobile network operators'profit by jointly optimizing function placement and optical resource allocation under latency, processing, bandwidth, and availability constraints. We formulate the problem as an Integer Linear Programming (ILP) model with two variants: one that employs unshared backups and another that uses a more cost-efficient shared backup scheme. To address ILP complexity, we develop a heuristic algorithm and a Genetic Algorithm (GA)-based metaheuristic that yields near-optimal solutions in real time. Extensive evaluations on topologies up to 128 nodes show that shared backup variants yield up to 18% higher profit, while maintaining up to 5-10% lower normalized CPU usage than unshared counterparts.
Mayank Ramnani, Shasank Dixit, Sushil K. Yadav et al.· 0 citations
Fairness is critical in delay-sensitive group applications—multiplayer online games, live collaborative editing, and distributed interactive simulations—where every participant should receive each message within a bounded delay and with minimal timing differences between recipients. We study fairness-aware multicast routing under two parameters: the maximum end-to-end delay (Δ) and the inter-destination delay variation (δ). Although several heuristics address this NP-hard problem and exact integer linear programs (ILPs) exist for the minimum-cost multi-constrained case, exact methods that directly optimize inter-destination delay variation under bounded delay remain underexplored. We present flow-based ILP formulations that treat Δ and δ as either objectives or constraints. Their feasible solutions are partial spanning hierarchies, a class that contains partial spanning trees as a special case; consequently a hierarchy optimum is, by construction, at least as good as the best tree-constrained solution. On proven-optimal instances, hierarchy optima reduce inter-destination delay variation considerably relative to the best tree-constrained solution. A sensitivity analysis, a real-topology study on the Abilene backbone, and an exact-ILP scalability study quantify and corroborate these gains.
Modern network infrastructures are undergoing a major transformation driven by Software Defined Networking (SDN). However, migration from legacy hardware to fully programmable architectures is typically incremental, resulting in hybrid environments where legacy routing protocols and centralized SDN controllers coexist. Managing these heterogeneous networks requires coordinated optimization across the physical infrastructure, control plane, and data plane. This thesis presents a multi-layer optimization framework for planning, deploying, and operating homogeneous and hybrid SDN environments. At the infrastructure layer, the Controller Placement Problem (CPP) is formulated as a multi-objective Integer Linear Programming (ILP) model and solved using exact ILP solvers and a localized Tabu Search approach. The framework determines controller placement and quantity to maximize network centrality and throughput while minimizing deployment cost and propagation delay. The ILP model reduces propagation delay by 16.4\% and 24.1\%, while the localized Tabu Search further improves transmitted data by 15.6\% and 26.2\% for the selected topology. At the control-plane layer, the thesis addresses protocol heterogeneity and route redistribution across administrative boundaries. Five routing protocols---BGP, EIGRP, IS-IS, OSPF, and RIP---are evaluated in terms of round-trip time (RTT), convergence delay, and a redistribution feasibility index(capturing topology compatibility, load sensitivity, and link stability). The optimization results show that while EIGRP provides strong proprietary performance, IS-IS emerges as the most resilient open-standard protocol for hybrid control planes. At the data-plane layer, the thesis develops two port-state-aware Fast Reroute (FRR) mechanisms for unpredictable link failures: PSA-FRR, a proactive rule-based approach for homogeneous networks, and PSAR-FRR, an automated deep neural network approach for hybrid environments. The neural model maps real-time interface status (port status) directly to backup egress paths using a formulated traffic engineering dataset. Experiments on the Abilene topology using Mininet, Ryu, OpenDaylight, and GNS3 show that both approaches restore traffic within 30--100~ms. PSAR-FRR achieves a data-plane switching latency of 0.123~ms, more than 70\% lower than PSA-FRR lookup latency and faster than the other evaluated machine learning methods. Overall, this thesis provides an end-to-end mathematical and machine learning framework for designing dependable, low-latency, and scalable SDN infrastructures.
To address the challenges of highly dynamic topology and limited resources in the Space-Ground-Sea Integrated Network, this study proposes a Hierarchical Regularized Routing Optimization (HRRO) algorithm based on Software-Defined Networking (SDN). Based on the SDN architecture, the algorithm organizes the network topology hierarchically and utilizes a dynamic weight matrix to capture real-time link conditions. It incorporates a congestion prediction model to avoid high-risk links proactively. An enhanced Dijkstra-based method is then applied for routing computation and optimization, enabling joint optimization of latency and resource utilization. Simulation results demonstrate that the proposed HRRO algorithm outperforms the conventional Dijkstra, OSPF, and RRO algorithms in reducing end-to-end latency, lowering packet loss rates, and improving network throughput.
Suming Li, Xuan Geng, Fang Cao· International Conference on...· 0 citations
We study the problem of Distributed Unit (DU) & Centralized Unit (CU) placement in Open RAN for reducing the energy footprint of the network, under explicit distance & bandwidth constraints on real-world Radio Unit (RU) topologies. We show that the DU / CU placement can be formulated as a minimum dominating set (MDS) problem on graphs derived from latency & bandwidth constraints, enabling exact solutions that minimize the number of deployed nodes. To further refine the placement while preserving this minimum deployment cardinality, we propose a sequential distance-weighted MDS approach that selects, among all minimum-cardinality solutions, the one reducing the load transport cost. We evaluate the proposed method on a real-world node topology using a detailed energy model capturing both computational and transport costs. The results show that the MDS formulation significantly reduces infrastructure footprint compared to a clustering-based baseline, leading to a global RAN energy gain of around 14%, while the sequential refinement provides additional gains reducing latency and transport energy cost.
Hiba Hojeij, A. Aravanis, Sahar Hoteit et al.· International Mediterranean...· 0 citations
Space-air-ground integrated networks (SAGINs) offer seamless three-dimensional coverage and strengthened flexibility, which are recognized as a core network architecture of 6G. Software-defined networking (SDN) and network function virtualization (NFV) are two enabling technologies for SAGINs that can be utilized to sequentially arrange virtual network functions (VNFs) into service function chains (SFCs) to provide users with resource-efficient and delay-optimized multi-source multicast request (MMR) services. However, SAGINs exhibit significant dynamism and heterogeneity, it brings great challenges when dynamically deploying the MMR’s source nodes and SFCs for fulfilling MMR routing. This paper investigates the multi-source multicast SFC embedding problem (MMSEP) considering the determination of the source nodes for MMR, VNFs placement, as well as network resources and delay constraints in the SDN/NFV-enabled SAGIN. Firstly, we define and formulate the MMSEP and demonstrate its NP-hardness. Subsequently, we employ a heuristic algorithm to assign the optimal source nodes for all multicast destination nodes and utilize the markov decision process (MDP) to simulate dynamic transitions in network states. Finally, we propose a deep deterministic policy gradient with attention mechanism (DDPG-AM) to address the MMSEP, aiming to minimize resource consumption costs and delays while maximizing the revenue of the internet service provider. The simulation results demonstrate that the proposed algorithm surpasses the state-of-the-art DDPG algorithm by approximately 27% in network utility, 17% in latency reduction, and 5% in acceptance ratio.
Liang Liu, Yejun He, Yujie Zhang et al.· IEEE Transactions on Network...· 0 citations