混合深度学习和优化的集群机制,用于负载平衡和容错在云计算中的容错
Vahini Siruvoru1,2, Shivampeta Aparna1
1Research Scholar, GITAM-School of Technology, Hyderabad, India.
概括
本研究介绍了一种混合深度学习算法,用于高效的云负载平衡. 这种新的方法优化了资源配置,提高了云服务中的能源效率和网络可靠性.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 云计算 云计算 云计算 云计算
背景情况:
- 负载平衡对于开发云服务的能源效率至关重要.
- 容错对于网络可靠性和可访问性至关重要.
研究的目的:
- 开发基于深度学习的混合负载平衡算法.
- 提高云环境中的能源效率和可靠性.
主要方法:
- 任务最初通过循环分配分配.
- 深度嵌入集群 (DEC) 使用CPU,内存和带宽识别过载/不足的虚拟机.
- 深度Q循环神经网络 (DQRNN) 考虑供应,需求,容量和故障耐受性的负载平衡.
主要成果:
- 混合型号实现了负载 (0.147),容量 (0.726),资源消耗 (0.527) 和成功率 (0.895) 的理想值.
- 在负载平衡和资源利用方面已证明有效.
结论:
- 拟议的深度学习算法有效地平衡云服务中的负载.
- 混合方法提高了能源效率和网络可靠性.
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