SpiWasp-CNTM: Enhanced Load Balancing in Cloud using Optimal Deep Learning Model

Load balancing is the strategic distribution of incoming computational workloads among virtual machines to prevent any single machine from becoming overwhelmed, thereby achieving equitable task allocation and optimizing resource utilization. This approach is essential for maintaining system efficiency and stability, allowing effective data processing without bottlenecks. However, current models face challenges related to resource management, often leading to bottlenecks and workload imbalances. To address these issues, the SpiWasp-optimized Hybrid Convolution Network with Bidirectional Long Short-Term Memory model (SpiWasp-CNTM) introduces significant advancements, adeptly processing both spatial and temporal patterns in time-series data, making it particularly effective for dynamic workloads. The model features a hierarchical solution update mechanism that strikes a balance between exploration and exploitation, enabling efficient navigation through complex optimization landscapes. Its phase-adaptive mechanism continuously fine-tunes scheduling decisions in response to real-time workload variations, enhancing precision and responsiveness. This adaptive capability significantly improves the system's ability to manage fluctuating resource demands, resulting in superior load balancing performance. The model's effectiveness is further evidenced by its impressive low error metrics, achieving a minimum Mean Absolute Error (MAE) of 1.12, Mean Absolute Percentage Error (MAPE) of 2.98, Mean Squared Error (MSE) of 4.37, and Root Mean Squared Error (RMSE) of 2.09, underscoring its reliability in optimizing load distribution.