云网络的新型负载平衡机制使用扩展和基于注意力的联合学习与Coati优化
Atul B Kathole1, Viomesh Kumar Singh2, Ankur Goyal3
1Department of Computer Engineering, Dr. D. Y. Patil Institute of Technology, Pimpri, Pune, 411018, India.
Scientific reports
|May 1, 2025
概括
本研究介绍了一种新的联合学习和区块链方法,用于云计算负载平衡. 它增强了资源预测和管理,提高了分布式系统的效率和性能.
科学领域:
- 云计算 云计算 云计算 云计算
- 网络负载平衡 网络负载平衡
- 分布式系统 分布式系统
背景情况:
- 负载均衡对于高效的云计算资源利用和系统性能至关重要.
- 随着云环境的扩展,有效的资源管理至关重要,特别是在分布式设置中.
- 现有的负载平衡方法在动态的大规模云网络中面临挑战.
研究的目的:
- 为云网络负载平衡提出一种新的资源预测模型.
- 将联合学习集成到区块链框架中,以实现安全的分布式管理.
- 在实时网络条件下增强负载分配的灵活性和效率.
主要方法:
- 利用扩展和基于注意力的1维卷积神经网络,使用双向长期短期记忆 (DA-DBL) 来进行资源预测.
- 在区块链框架内集成的联合学习,用于安全和分布式的资源管理.
- 采用随机对立科蒂优化算法 (RO-COA) 进行自适应负载分布.
主要成果:
- 拟议的DA-DBL模型根据处理时间,反应时间和可用性准确预测资源需求.
- 联合学习和区块链集成确保了安全和分散的负载均衡.
- RO-COA算法促进了有效的负载分配,适应实时网络变化.
- 包括主动服务器,makepan,QoS,资源利用率和功耗在内的评估指标显示,与现有方法相比,性能优越.
结论:
- 联合联合学习和基于RO-COA的负载平衡方法为云资源管理提供了强大的解决方案.
- 这种方法显著提高了系统可靠性,资源效率和云计算环境中的整体性能.
- 该研究表明,将先进的AI和区块链技术集成到下一代云基础设施中的有效性.
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