2026· IEEE Open Journal of the Communications Society· Vol 7, pp. 10012-10035· 0 citations· 41 references
TL;DR
The results indicate that iScavenger provides configurable operating points in the latency–utilization trade-off, limiting additional Sticky-flow RTT while achieving higher background throughput than conservative baseline policies, and highlight the potential of short-term traffic-demand prediction for proactive contention management in ATSSS-enabled multi-access networks.
Abstract
3GPP Access Traffic Steering, Switching, and Splitting (ATSSS) enables traffic to be distributed across heterogeneous 3GPP and non-3GPP access networks to improve performance, reliability, and resilience. ATSSS can use multipath transport protocols such as Multipath QUIC (MP-QUIC), where packet scheduling plays a central role in determining latency and resource utilization for delay-sensitive applications. Many existing MP-QUIC scheduling policies rely on instantaneous path measurements or fixed rules rather than forecasts of future application demand. In multi-flow scenarios, such decisions can lead either to contention on the preferred low-latency path and transient latency inflation for priority traffic or to overly conservative use of available capacity. This paper proposes iScavenger, a predictive, machine-learning-based multi-flow scheduling policy for ATSSS environments. iScavenger employs a Long Short-Term Memory (LSTM) model to forecast near-future bandwidth demand for delay-sensitive Sticky traffic. Based on this prediction, background packets are admitted to the preferred low-latency path only when sufficient residual capacity is expected to remain; otherwise, they are steered to the alternative path. The policy is implemented within the Monty MP-QUIC framework and evaluated in a controlled Mininet testbed using traffic traces from the online game League of Legends, with fixed and variable path capacities and controlled jitter and packet loss. The results indicate that, under the evaluated conditions, iScavenger provides configurable operating points in the latency–utilization trade-off, limiting additional Sticky-flow RTT while achieving higher background throughput than conservative baseline policies. These findings highlight the potential of short-term traffic-demand prediction for proactive contention management in ATSSS-enabled multi-access networks.
The rapid growth of multimedia streaming poses critical challenges, including bursty traffic and congestion, leading to playback delays. The existing separate prediction and control mechanisms for multimedia traffic scheduling, which are based on software-defined networks (SDN), are unable to proactively manage bursty traffic under uncertain conditions. This limitation is particularly evident in SDN-enabled backbone and multimedia-aware access networks, which typically assume centralized control and stable topologies. They lack integration of traffic prediction, traffic shaping, and real-time perception scheduling through reinforcement learning, resulting in low efficiency when exploring multiple paths in dynamic networks. To address this challenge, we propose PPO-MS (Proximal Policy Optimization-based Multimedia Scheduler), an SDN-based multimedia traffic scheduling algorithm integrating three key innovations: 1) A novel LSTM+HTB synergy where LSTM’s confidence intervals dynamically adjust HTB (Hierarchical Token Bucket) shaping parameters, enabling adaptive rate control under prediction uncertainty and overcoming the limitations of static LSTM+HTB hybrids; 2) A Deep Reinforcement Learning (DRL)-optimized path pruning method that reduces state and action spaces by generating a constrained set of $k$ disjoint candidate paths via an improved redundant tree algorithm. Unlike traditional multi-path schemes, this method tightly couples path preselection with the RL decision loop for adaptive, context-aware routing; 3) Generalized Advantage Estimation (GAE)-accelerated PPO for stable convergence in dynamic environments. In contrast to prior works (e.g., LSTM+RL for QoE or standalone tree algorithms), PPO-MS uniquely unifies these modules through confidence-aware traffic shaping and hierarchical decision-making, validated via comparative experiments. Results demonstrate that PPO-MS, through the synergistic integration of confidence-aware traffic shaping and DRL-optimized path pruning, significantly outperforms decoupled baselines. In particular, via isolation studies against simpler alternatives (e.g., mean-prediction and fixed-margin shaping), the confidence-aware shaping mechanism is validated to be superior under bursty traffic conditions. Overall, PPO-MS reduces end-to-end latency by 17.3% and packet loss by 32.4% while achieving 24.4% better load balancing during traffic bursts.
Point-to-multipoint (P2MP) coherent optical architectures that use digital subcarrier multiplexing support efficient aggregation in metro-access networks. Proactive provisioning of hub capacity requires accurate per-spoke demand forecasts. However, spoke nodes carry heterogeneous traffic profiles such as business, residential, and mixed, with distinct diurnal patterns and forecast difficulty. We examine whether a single multivariate Long Short-Term Memory (LSTM) network can capture these profile-specific dynamics without explicit labels, and quantify how the resulting per-profile forecast accuracy propagates to operational P2MP resource provisioning metrics. The forecasts feed a greedy reconfiguration-aware heuristic algorithm, which jointly decides light-tree assignment at each control epoch while accounting for rerouting, resizing, and point-to-point forwarding penalties. Evaluation on a 30-node TID-derived metro topology with 300 spokes with different OSNR budgets shows that the joint LSTM reduces per-profile mean absolute error by 13-22% over a seasonal baseline, with the largest gains on business spokes. LSTM-driven provisioning achieves 13-15% lower total operational cost than seasonal-driven provisioning and 29-30% savings over static peak allocation, while maintaining demand violations below 1.2%. The LSTM reduces reconfiguration churn by 12–26% per profile over seasonal and lagged baselines, largest on mixed spokes whose composite weekday/weekend pattern is hardest for seasonal forecasts to track.
P. Soumplis, Konstantinos Christodoulopoulos, K. Yiannopoulos et al.· International Symposium on C...· 0 citations
The multi-agent transformer (MAT) is adopted to model inter-queue dependencies via attention over agents' observations and actions, enabling implicit coordination across heterogeneous co-located XR applications and results show that the proposed method outperforms baselines.
Marcos Carvalho, Fatih Temiz, Shavbo Salehi et al.· 0 citations
A slice-aware deep learning framework for the joint prediction of traffic demand and multiple KPIs within a simulation-driven environment and shows that deep learning models more effectively capture nonlinear slice-level dynamics compared with traditional forecasting approaches.
Sultan Ertas, B. Cavusoglu· IEEE Access· 0 citations
A hybrid reinforcement learning (RL) framework that jointly controls queue management and bandwidth allocation in bursty multi-service networks and demonstrates the effectiveness of coordinated learning-based control for stable and QoS-aware operation in bursty networked systems.
T. Khan, Babar Shah, Taimur Karamat et al.· Computing· 0 citations
Adaptive queue management must balance throughput, delay, packet loss, and fairness under changing traffic and resource conditions. This paper proposes TS-D3QS, a traffic-state-aware queue scheduler that formulates multi-queue resource allocation as a discrete reinforcement-learning problem. The scheduler observes normalized queue and port features and selects one of 16 interpretable allocation profiles, each jointly specifying bandwidth shares, shared-buffer shares, and priority multipliers. A Dueling Double-DQN learner separates state value from action advantage and use a Double-DQN target to reduce value overestimation. Experiments in a reproducible four-queue simulator under bursty and non-stationary traffic show that TS-D3QS reduces latency by 7.54%, reduces aggregate loss by 3.21%, improves Jain fairness by 3.30%, and improves reward by 6.01% over vanilla DQN, while classical CoDel-like and WFQ-like rules remain competitive on selected objectives.
Hao-Yan Wang, Q. Guan, Dapeng Yan et al.· 2026 8th International Confe...· 0 citations
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