Latency-Sensitive and Resource-Efficient Parallel VNF Placement in Mobile Edge Networks: A Dynamic Graph Weighting Approach

Proximal Policy Optimization for Latency-Aware Service Function Chain Placement in Edge-Enabled Networks

This work proposes an enhanced Proximal Policy Optimization (PPO) framework for resource-aware and latency-sensitive SFC placement in edge-enabled networks, and demonstrates the applicability of the proposed framework in mission-critical and latency-sensitive service environments.

Split-Aware Function Placement with Availability Guarantees and Optical Provisioning in vRANs

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.

Energy-efficient DAG scheduling based on duplication and slack-aware DVFS in heterogeneous fog–edge–cloud environments

Results show that ECD-DVFS provides a balanced trade-off among performance, energy efficiency, reliability, cost, and QoS through adaptive and energy-aware workflow scheduling in hybrid fog–edge–cloud environments.

CERLA-SFC: hierarchical orchestration of cost-efficient, reliable and low-latency SFCs in multi-access edge computing

Joint Task Offloading and Resource Allocation with Data Caching in UAV-Aided Mobile Edge Computing Networks for Latency-Sensitive Applications

Simulation results confirm that the proposed JORC framework substantially reduces latency, energy consumption, and overall system cost, while increasing the successful task completion ratio compared to existing baseline approaches.

Cost-Optimal Cross-Cloud Data Transfer Scheduling in Jointcloud Environments with Time-Dependent Pricing

JointCloud environments, including multi-cloud and federated cloud systems, increasingly rely on high-performance networks (HPNs) to support large-scale cross-cloud data transfers. In such settings, advance bandwidth reservation with timedependent pricing is essential for cost-efficient and predictable data movement, where the transfer cost depends on both dynamic link prices and path reconfiguration overhead. This paper investigates the optimal scheduling of the VPFB BRR-MinC, where VPFB (Variable Path, Fixed Bandwidth) allows routing paths to change across time slots while maintaining a constant reserved bandwidth, and BRR-MinC seeks a minimum-cost schedule for deadline-constrained data transfers. We formalize a timedependent cost model incorporating slot-varying edge weights and switching penalties, and prove that the problem is NPcomplete. To address the temporal coupling introduced by switching costs, we develop a segmentation-based dynamic programming framework and propose a scalable heuristic, Heu-VPFB-MinC-TD-S. Simulation results on an ESnet-inspired topology show that the proposed method achieves identical feasibility while reducing total transfer cost compared with a greedy baseline, at the expense of moderate additional runtime. These results demonstrate the effectiveness of segmentation-aware optimization for cost-efficient cross-cloud data transfer in JointCloud systems.