深度学习和优化实现了多目标,用于云计算中的任务调度
Dinesh Komarasamy1, Siva Malar Ramaganthan2, Dharani Molapalayam Kandaswamy3
1Department of Computer Science and Engineering, Kongu Engineering College, Erode, India.
概括
本研究介绍了一种新的云计算任务调度模型,使用混合分数火甲虫优化 (FFBO) 和深度学习 (DL) 来提高效率. FFBO-DL模型优化了基于可靠性,成本,能量和时长的任务分配,实现了卓越的性能.
科学领域:
- 云计算 云计算 云计算 云计算
- 人工智能的人工智能
- 优化算法 优化算法
背景情况:
- 在云计算中,任务调度对于有效的资源配置至关重要.
- 现有的方法往往难以平衡成本,能源和性能等多个目标.
- 深度学习和先进的优化技术为改进调度提供了潜力.
研究的目的:
- 提出一种新的混合模型,用于云计算中的多目标任务调度.
- 整合深度学习用于能源预测和资源配置优化算法.
- 通过考虑任务和虚拟机参数来提高整体系统效率.
主要方法:
- 一种混合分数火甲虫优化 (FFBO) 算法,结合了甲虫优化 (DBO),火甲虫搜索算法 (FSA) 和分数计算 (FC).
- 深度残留网络 (DRN) 用于预测能源消耗.
- 深度Feedforward神经网络融合了长期短期记忆 (DFNN-LSTM) 用于任务调度.
- 考虑任务参数 (EFT,EST,任务长度,优先级,运行时间) 和VM参数 (CPU,内存,带宽,容量).
主要成果:
- 拟议的DFNN-LSTM+FFBO模型在关键指标上表现出卓越的表现.
- 实现了0.188的产量,0.950J的能源消耗,0.238.8的资源利用率.
- 与现有的任务安排方法相比,显示出了显著的改进.
结论:
- 集成的DFNN-LSTM+FFBO模型有效地解决了云计算中的多目标任务调度.
- 混合方法显示出优化云资源管理的前景.
- 进一步的研究可以探索可扩展性和现实世界的部署.
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