Jul 2026· 2026 International Conference on Intelligent and Sustainable AI Systems (ICOSAAS)· pp. 150-158· 0 citations· 12 references
Abstract
Edge network controllers must allocate bandwidth under rapidly changing traffic demand while avoiding both underprovisioning and excessive overprovisioning. This paper presents a forecast-driven adaptive bandwidth allocation frame-work that converts short-horizon traffic predictions into edge resource-control decisions evaluated in an analytical QoS simulator. Using a PCAP-derived traffic corpus, reactive rolling-mean allocation, persistence-based allocation, LSTM-driven allocation, CNN–LSTM-driven allocation, a TensorFlow Lite-compatible CNN–LSTM policy, hybrid max policies, adaptive safety-factor controllers, and a perfect-forecast + SF reference are compared. Results show that forecast-driven allocation reduces average latency, packet loss, jitter, and SLA violations compared with reactive control when forecast bias is favorable, while adaptive safety-factor tuning reduces underprovisioning without unbounded bandwidth waste. Persistence and standalone LSTM fixed-safety policies are reported separately as burst-sensitive failure modes; they are excluded from primary charts and capped-comparison tables because rare underprediction produces extreme uncapped analytical delays. The analysis further shows that allocation quality depends not only on RMSE but also on forecast bias, underprediction rate, and safety-factor behavior. This study does not introduce a new forecasting architecture; forecasting models are used as input predictors. The simulator is analytical and intended for comparative policy evaluation, not live deployment measurements.
Accurate network traffic forecasting is fundamental to Quality of Service enforcement, proactive congestion control, and dynamic resource allocation in modern backbone and software-defined networks. However, existing approaches often lack adaptability to non-stationary traffic patterns and fail to provide a consistent comparative evaluation across diverse models under unified experimental conditions. This paper presents an adaptive, data-driven framework that integrates Bidirectional LSTM (Bi-LSTM), LSTM, Gated Recurrent Units (GRU), Random Forest, XGBoost, Support Vector Regression (SVR), and classical ARIMA regressors for short- and medium-term traffic forecasting. The proposed architecture couples multi-scale temporal feature extraction with a feedback-driven online retraining loop, enabling continuous adaptation to distributional shifts. Extensive experiments are conducted on two publicly available datasets CAIDA Equinix backbone traces and the MAWI traffic archive comprising over 72 hours of flow-level measurements at one-minute resolution. Bi-LSTM achieves the lowest RMSE of 0.0287 Gbps and the highest $R^{2}=0.9714$, outperforming ARIMA by $\mathbf{7 5 . 2 \%}$ and vanilla LSTM by $\mathbf{8 . 0 \%}$. All results are confirmed via paired Diebold-Mariano (DM) tests and Student’s t-tests $(p \lt 0.01)$. System inference latency of 2.3 ms per batch satisfies real-time SDN control-plane requirements. Code and preprocessing scripts will be made publicly available to ensure full reproducibility.
E. Chithra, G. C. Bharathi, Sankara Rao Allada et al.· International Conference on...· 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
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.
Shah M. Emad Uddin, Karl-Johan Grinnemo, Arunselvan Ramaswamy et al.· IEEE Open Journal of the Com...· 0 citations
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
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
Cloud applications experience frequent and sometimes unpredictable shifts in demand due to user activity, daily usage cycles, and sudden workload spikes. Traditional autoscaling mechanisms used in cloud environments mostly follow reactive, threshold-based rules. They trigger scaling only after resources begin to saturate, which often leads to slow provisioning, increased response times, and occasional SLA violations during high-traffic periods. When demand drops, these systems may still allocate more resources than necessary, resulting in avoidable costs. To address these issues, this work introduces a predictive autoscaling approach built using a combined Transformer-LSTM model. The fusion model is designed to capture both long-term workload trends and short-term sequential patterns, giving it a more accurate view of workload behavior. A cost optimization function is added to translate multi-step workload predictions into suitable virtual machine allocation decisions while considering cloud pricing and SLA constraints. Bayesian optimization is then used to fine-tune the forecasting model and COF parameters, ensuring an effective balance between accuracy, SLA compliance, and cost savings. Experiments using Google Cluster Trace data show encouraging improvements: forecasting accuracy increases by 18-25%, SLA violations drop by 40-55%, and total costs reduce by 22-35% compared to traditional reactive and single-model predictive methods.
Unknown authors· international journal of eng...· 0 citations
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