What type of study is this?

This is a Experimental Study study (also classified as: Quantitative Study).

September 30, 2025

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

Key Points

The proposed framework significantly improves load balancing in cloud environments, enhancing performance.
Simulation results highlight a notable reduction in latency and improved throughput with the DL-RL approach.
The framework employs an attention-based LSTM for workload forecasting and a lightweight CNN for task classification.
Optimization through Double Deep Q-Network shows enhanced decision-making in adaptive load balancing.

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.

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