This paper presents a predictive analytics framework for dynamic resource allocation in next-generation networks, with a focus on 5G and 6G scenarios. The proposed approach uses Long Short-Term Memory (LSTM) models to predict traffic patterns and proactively support automated resource management decisions in network environments. Building on our previous work, we introduce a selective knowledge transfer mechanism, termed “cognitive transfer,” which allows for the reuse of relevant components from previously trained models. This method consolidates multiple models into a compact, generalized form and transfers only the most relevant segments to new traffic scenarios, significantly reducing the need for training from scratch, especially when local data is limited. This maintains an initial consolidated (base) model that is adapted to new traffic scenarios by selecting the corresponding segment from the most relevant parts of the model, identified via a scoring metric. To complement this, a decision-based model retraining strategy is integrated to monitor prediction accuracy, triggering updates only when performance degrades to reduce computational overhead. The framework is evaluated using the Abilene network topology and traffic dataset. Results demonstrate that the approach maintains high prediction accuracy while minimizing under-provisioning, which is critical for avoiding packet loss and ensuring service continuity in high-reliability applications. Across four network links, our method reduced the average number of re-trainings by 1.8× under relaxed thresholds compared to full model transfer, while the prediction error increased by a negligible 0.0008.
Tabatabaei et al. (Tue,) studied this question.