在多种虚拟化云计算环境中使用优化自我注意力渐进式生成对抗网络进行能源和时间意识的调度
G Senthilkumar1, S Anandamurugan1
1Department of Information Technology, Kongu Engineering College, Perundurai, India.
概括
本研究介绍了SAPGAN-DMA-DAS-HVCC,这是云计算的先进调度算法. 它大大降低了成本,并在异质的虚拟环境中提高了性能.
科学领域:
- 计算机科学 计算机科学
- 云计算 云计算 云计算 云计算
- 人工智能的人工智能
背景情况:
- 云计算的采用正在增长,导致复杂,异质的虚拟化环境.
- 在这些环境中优化能源消耗是具有挑战性的,因为工作负载的变化.
- 现有的调度算法难以平衡产量和能源效率.
研究的目的:
- 为异质的虚拟云计算环境提出一个新的调度算法.
- 为了同时优化能源消耗和makepan.
- 提高动态云基础设施中任务调度的效率.
主要方法:
- 基于自我注意的渐进式生成对抗网络 (SAPGAN) 已被开发用于活动安排.
- 矮人蒙古斯算法被用来优化SAPGAN的重量参数.
- 能源和截止日期意识调度 (DAS) 被整合到异质虚拟化云计算 (HVCC) 框架中.
主要成果:
- 拟议的SAPGAN-DMA-DAS-HVCC方法显示出更高的右倾造型范围.
- 观察到显著的成本降低:比现有模型低31.52%,33.28%和29.14%.
- 该算法有效地平衡了能源消耗和任务完成时间.
结论:
- 在异质的虚拟云环境中,SAPGAN-DMA-DAS-HVCC为任务调度提供了卓越的解决方案.
- 集成SAPGAN和矮人蒙古 algorithm优化了性能和能源效率.
- 这种方法解决了云资源管理和降低成本的关键挑战.
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