A DUAL-BRANCH NEURAL FORECASTING ARCHITECTURE WITH SESSION BEHAVIORAL PROFILING AND ELASTIC WEIGHT CONSOLIDATION FOR ADAPTIVE CLOUD RESOURCE ALLOCATION

Abstract

The AI-driven Adaptive Resource Allocation Engine (AARAE) provides proactive resource control for multi-tenant cloud environments. Reactive autoscaling systems suffer from allocation lag: scaling actions trigger only after hypervisor metrics cross predefined thresholds. AARAE extracts early-warning behavioral signatures from application-layer session request graphs and Shannon entropy metrics, detecting traffic transitions before physical hardware saturation occurs. The forecasting pipeline couples a two-layer gated recurrent unit (GRU) network for short-horizon dynamics with an eight-head transformer encoder for long-horizon trends, and a weighted-error meta-network arbitrates between the two branches. During online adaptation, AARAE regularizes neural parameter updates through Elastic Weight Consolidation (EWC) based on Fisher information matrices estimated over a rolling 30-day window; it maintains user session centroids independently, through recency-weighted exponential averaging. A greedy optimization algorithm computes workload redistribution commands over precomputed compressed sparse row (CSR) constraint matrices. Across 1,847 physical nodes and fifty independent trials, AARAE reaches average resource utilization of 82.4%, reduces the SLA violation rate to 0.84%, limits forecasting MAPE during behavioral regime shifts to 9.1%, and sustains sub-second scheduling latency for up to 50,000 workload units.