DCN-LB: A Deep Learning-Driven Cloud Load Balancing Framework for Efficient Resource Optimization
Year: 2025
Type: article
Abstract: 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.
DOI: 10.14419/535sc283
Authors: Damodhar M., Ch. D V Subba Rao
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4414611099
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