Forecast-Driven Adaptive Bandwidth Allocation for Edge Networks using Lightweight CNN–LSTM Traffic Prediction

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.