Design and Implementation of Hybrid Adaptive Neural Architecture for Self-Absorption in Virtual Machines

Abstract

This study introduces a Hybrid Adaptive Neural Architecture designed to address the dynamic resource management challenges in Virtual Machines (VMs). Current static and heuristic-based approaches are insufficient for adapting to real-time workload variations, resulting in inefficiencies, latency, and resource contention. The proposed architecture leverages neural networks, including convolutional and recurrent layers, integrated with adaptive mechanisms such as reinforcement and transfer learning, to enable self-absorptive capabilities in VMs. This self-adaptation allows VMs to autonomously learn from operational data, predict resource demands, and adjust allocations in real-time, optimizing performance and minimizing overhead. Experimental evaluation across diverse workload patterns demonstrated the architecture's effectiveness. For burst workloads, the proposed system achieved a 98.6% success rate, outperforming heuristic methods (77.3%) and static allocation (64.2%). Under steady workloads, it maintained 94.9% throughput consistency, compared to 81.7% and 70.3%, respectively. The architecture reduced ephemeral workload allocation lag to 28.7 ms, significantly outperforming heuristic (115.6 ms) and static approaches (205.4 ms). Additionally, the proposed system improved resource utilization, achieving 84.7% CPU efficiency and 92.4% memory efficiency, while maintaining a low latency of 48.6 ms. These results validate the system's ability to dynamically allocate resources efficiently, adapt to workload variability, and enhance overall VM performance. The findings set a benchmark for neural-based resource management in virtualized environments, paving the way for scalable, autonomous solutions in modern computing infrastructures.