Tools and libraries for integration.

The increasing dependence on cloud computing as a cornerstone of modern technological infrastructures has introduced significant challenges in resource management. Traditional load-balancing techniques often prove inadequate in addressing cloud environments’ dynamic and complex nature, resulting in suboptimal resource utilization and heightened operational costs. This paper presents a novel smart load-balancing strategy incorporating advanced techniques to mitigate these limitations. Specifically, it addresses the critical need for a more adaptive and efficient approach to workload management in cloud environments, where conventional methods fall short in handling dynamic and fluctuating workloads. To bridge this gap, the paper proposes a hybrid load-balancing methodology that integrates feature selection and deep learning models for optimizing resource allocation. The proposed Smart Load Adaptive Distribution with Reinforcement and Optimization approach, SLADRO, combines Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) algorithms for load prediction, a hybrid bio-inspired optimization technique—Orthogonal Arrays and Particle Swarm Optimization (OOA-PSO)—for feature selection algorithms, and Deep Reinforcement Learning (DRL) for dynamic task scheduling. Extensive simulations conducted on a real-world dataset called Google Cluster Trace dataset reveal that the SLADRO model significantly outperforms traditional load-balancing approaches, yielding notable improvements in throughput, makespan, resource utilization, and energy efficiency. This integration of advanced techniques offers a scalable and adaptive solution, providing a comprehensive framework for efficient load balancing in cloud computing environments.

随着云计算成为现代技术基础设施的核心基石，对其依赖程度的不断加深为资源管理领域带来了诸多严峻挑战。传统负载均衡技术往往难以适配云计算环境的动态复杂特性，进而导致资源利用率低下、运营成本攀升。针对上述局限性，本文提出一种融合先进技术的新型智能负载均衡策略。具体而言，云计算环境中的工作负载具备动态波动特性，传统方法在此类场景下表现欠佳，因此亟需更具自适应能力与高效性的工作负载管理方案。为填补这一研究空白，本文提出一种集成特征选择与深度学习模型的混合负载均衡方法，用于优化资源分配。所提出的带强化与优化的智能负载自适应分发策略（Smart Load Adaptive Distribution with Reinforcement and Optimization, SLADRO），结合卷积神经网络（Convolutional Neural Networks, CNN）与长短期记忆网络（Long Short-Term Memory, LSTM）算法实现负载预测，采用混合仿生优化技术——正交阵列与粒子群优化（Orthogonal Arrays and Particle Swarm Optimization, OOA-PSO）实现特征选择，并借助深度强化学习（Deep Reinforcement Learning, DRL）完成动态任务调度。在名为谷歌集群追踪数据集（Google Cluster Trace dataset）的真实世界数据集上开展的大规模仿真实验表明，SLADRO模型的性能显著优于传统负载均衡方法，在吞吐量、完工时间、资源利用率与能源效率等方面均实现了显著提升。该集成先进技术的方案提供了可扩展且自适应的解决方案，为云计算环境下的高效负载均衡构建了一套完整的框架。