Jul 2026· Fall Joint Computer Conference· pp. 450-455· 0 citations· 23 references
Abstract
Software defined networks offer global visibility, yet centralized control loops are too slow for transient congestion and bursty traffic dynamics. Existing learned traffic control schemes often rely on offline training, making them fragile under distribution shifts. We present EnvelopeTC, a hierarchical SDN traffic control framework that enables local online adaptation under centralized policy control. Its key abstraction is a policy envelope: the controller compiles network wide intent into bounded per path action spaces, while edge agents learn and execute metering, queueing, and rerouting decisions only within those bounds. Policy envelopes also make local actions auditable and reversible when they affect shared bottlenecks. Evaluation on a 1,024 host software SDN testbed shows that EnvelopeTC improves average core link utilization by 35.5% over Static ECMP and 18.3% over Centralized TE. It reduces elephant flow P99 FCT by 34.3% over end host congestion control, lowers SLA violations from 18.2% to 6.8%, and uses less than 2% CPU and 12 MB memory per edge agent. The source code is available in an anonymized repository at https://anonymous.4open.science/r/JCC2026-EnvelopeTC/.
In data centers, large-scale many-to-one traffic can rapidly exhaust switch buffers and trigger priority-based flow control (PFC) pause, resulting in increased flow completion time (FCT) for uncongested flows. To address this issue, we propose an innovative switch-side fast and accurate flow control (FAFC) scheme. By differentially allocating pause time for each port during congestion, FAFC can minimize the performance loss for uncongested flows. Furthermore, FAFC is also coupled with an effective queue length prediction algorithm to enable proactive and reliable estimation of the congestion level. Extensive system-level simulations demonstrate that FAFC can flexibly allocate pause times across congested ports, which are not only compatible with existing PFC but also do not require per-flow states. We implemented FAFC in P4 programmable switches, showing it as lightweight flow control method that is portable for implementation in hardware. Remarkably, our large-scale simulations illustrate that compared to traditional PFC, FAFC improves the average FCT slowdown and 95% FCT slowdown by 10.6% and 23.3%, respectively, under Hadoop workload when performing HPCC congestion control.
Chengdi Lu, Yuang Chen, Fangyu Zhang et al.· IEEE Transactions on Network...· 0 citations
Spectrum admission in low-latency mobile networks increasingly relies on fast control loops under time-varying traffic and radio conditions. A key challenge in such settings is reconfiguration churn: small score fluctuations near the admission boundary can repeatedly flip the marginal admitted request, even when the resulting utility gain is negligible. These boundary-level replacements are disproportionately expensive because admission and eviction trigger higher-layer control procedures, signaling exchanges, and coordination overhead. To address this problem, we propose TOA-S, a churn-aware admission primitive for latency-bounded mobile control. TOA-S targets cellular radio access network (RAN) deployments operating over licensed spectrum, where control loops of the ultra-reliable low-latency communication class at the near-real-time RAN Intelligent Controller must complete within sub-second decision windows. TOA-S preserves the greedy allocation structure for the responsive core of the admitted set and applies stabilization only at the admission boundary, in a single pass per decision epoch, without iterative optimization or learning. We show that TOA-S incurs only an $\varepsilon $ -bounded utility deviation, modifies at most one membership decision per epoch, and suppresses oscillatory replacements under persistent boundary near-tie conditions. Simulations on synthetic workloads with measurement-verified parameters show that TOA-S substantially reduces reconfiguration churn while maintaining spectrum utilization and latency-compatible execution.
Chi-Jen Wu· IEEE Open Journal of the Com...· 0 citations
In multi-tenant network environments, deploying Linear Quadratic Regulators at scale is impeded not by theory but by a practical bottleneck: cost matrices require per-instance manual tuning, yet optimal parameters vary across tenants and evolve as traffic dynamics change, making per-instance configuration infeasible at large scale. Prior adaptive and gain-scheduling approaches either require offline enumeration of operating regimes [1] or designer-specified tuning parameters [2], and prior work has not addressed autonomous Q-matrix discovery from online statistical observation of plant behavior. We present a trust-based adaptation mechanism that continuously monitors traffic predictability via Coefficient of Variation analysis and autonomously maps observed statistics to LQR cost matrix parameters, selecting among provably stable controllers without any prior knowledge of tenant traffic profiles. Using NS-3 simulations with multi-phase dynamic traffic and scalability experiments across varying tenant populations, we show the system autonomously discovers the full control spectrum from uniform initialization, significantly reduces queue occupancy and latency compared to manually-tuned fixed LQR, and maintains equivalent fairness at scale. This work demonstrates that statistical plant characterization can drive zero-touch controller synthesis with formal stability guarantees, offering a practical path to autonomous LQR deployment where per-instance expert tuning is infeasible.
Ahmed Ben Ali, Yann Labit, Bénôit Nougnanke· International Conference on...· 0 citations
Urban traffic congestion imposes significant economic and environmental costs worldwide. Conventional adaptive signal controllers-including actuated systems and deep reinforcement-learning (DRL) approaches-either collapse under saturation or lack formal verifiability, making them unsuitable for safety-critical infrastructure. This paper presents the Traffic Light Management Operating Framework (TLMOF), a signal control architecture that maps classical operating-system (OS) scheduling theory directly onto intersection management. Vehicles are modeled as processes, approach lanes as ready queues, and the intersection controller as a CPU scheduler. The TLMOF Hybrid Kernel selects signal phases by maximising a modified pressure function combining Max-Pressure (MP) throughput control with Weighted Fair Queuing (WFQ) anti-starvation. Gridlock is formally characterised as a Coffman deadlock, detected via Wait-for-Graph (WFG) depth-first search in O(V+E) time, and resolved through a formally specified Flush Phase. With fairness weight β > 0, the kernel provably guarantees bounded delay for all movements (AntiStarvation Theorem). SUMO 1.26 validation across three demand scenarios demonstrates a 377× average-delay reduction over fixed-time under saturation and a 25% Jain's Fairness Index improvement over pure Max-Pressure (0.909 vs. 0.727) under asymmetric demand while reducing average delay by 56% (3.62 s vs. 8.15 s). A sensitivity analysis of (α, β) confirms that the recommended default (0.7, 0.3) achieves a robust balance between throughput and fairness.
Kumaran K, Richika Rani, Mokshdaa Gupta et al.· 2026 7th International Confe...· 0 citations
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.
Shah M. Emad Uddin, Karl-Johan Grinnemo, Arunselvan Ramaswamy et al.· IEEE Open Journal of the Com...· 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