Memory Spike Detection System Using Gated Recurrent Unit and Transformer

An intelligent deep-learning system called the Memory Spike Detection System was created to spot abrupt and unusual changes in memory utilization in computer environments. Conventional monitoring systems mostly rely on threshold-based warnings, which frequently miss small or quickly changing anomalies. Our suggested approach combines the advantages of Transformer and Gated Recurrent Units (GRU) architectures for effective temporal pattern learning and long-range dependency modeling in order to overcome this constraint. While the Transformer improves detection accuracy by modeling global attention throughout the memory usage timeline, GRU assists in capturing short-term sequential alterations at a lower computational cost. When combined, they allow for accurate forecasting and early detection of memory increases. This solution is appropriate for edge computing, servers, cloud platforms, and IoT devices for real-time monitoring. When contrasted to conventional machine-learning methods, experimental results demonstrate increased accuracy, fewer false alarms, and quicker anomaly identification. The suggested mixed paradigm shows great promise for preventing memory overload-related system failures and alert system management. Furthermore, the system's lightweight and scalable design makes it appropriate for deployment across edge computing devices, enterprise servers, cloud infrastructures, and IoT platforms that require real-time monitoring and rapid reaction. The suggested approach efficiently minimizes memory overload-related failures, increases system reliability, and enhances proactive alert management by allowing for early identification of anomalous memory behaviour. Overall, the hybrid GRU-Transformer framework is a promising and useful method for detecting intelligent memory anomalies in dynamic computing environments.