DCN-LB: A Deep Learning-Driven Cloud Load BalancingFramework for Efficient Resource Optimization

Cloud computing is a technology that meets the needs of a vast number of users. Predicting ‎workload and scheduling are often the elements that determine cloud performance. This study ‎addresses the challenge of efficient load balancing in scalable cloud environments, where ‎traditional methods fail under dynamic workloads, leading to resource wastage and ‎performance degradation. To overcome this, an advanced framework integrating Deep ‎Learning (DL) and Reinforcement Learning (RL) is proposed. Simulation data on CPU usage, ‎memory, network traffic, and execution times are collected and preprocessed using mean ‎imputation and Min-Max normalization. A Sliding Window Approach with Multi-Scale ‎Convolutional Bidirectional LSTM (MS-Conv-BiLSTM) is used for time-series feature ‎extraction. An Attention-based LSTM forecasts workload levels, while a lightweight CNN ‎assists in task classification. Adaptive load balancing decisions are optimized using the Double Deep Q-Network (DDQN), aiming to reduce latency and response time while maximizing throughput and resource utilization. Experimental results confirm that the DL-RL framework ‎significantly enhances real-time load balancing performance in cloud environments‎.