云计算中的优先任务调度算法使用猫群优化
Sudheer Mangalampalli1, Sangram Keshari Swain2, Tulika Chakrabarti3
1School of Computer Science and Engineering, VIT-AP University, Amarvati 522237, Andhra Pradesh, India.
Sensors (Basel, Switzerland)
|July 14, 2023
概括
这项研究介绍了一种新的云任务调度算法,使用猫群优化来最大限度地减少产量,能源使用和SLA违规. 新方法有效地对任务进行优先排序,以更好地管理云资源和提高服务质量.
科学领域:
- 云计算是一种云计算.
- 资源管理 资源管理
- 算法优化的算法优化
背景情况:
- 不有效的云资源调度会影响服务质量,增加能源消耗,延长任务完成时间 (makespan).
- 尽量减少服务级别协议 (SLA) 违规行为对于云环境至关重要,它会影响产量,能源使用和整体服务质量.
- 现有的调度算法提供了近乎最佳的解决方案,但往往忽视了任务优先级和VM适用性.
研究的目的:
- 为云平台开发一种新的任务调度算法,该算法根据计算的VM优先级对任务进行优先排序.
- 通过最大限度地减少产量,能源消耗和SLA违规,提高云服务质量.
- 为了利用猫群优化算法来实现高效的云任务调度.
主要方法:
- 开发了一个新的任务调度算法,包含任务优先级和计算VM优先级.
- 模拟了使用猫群优化 (CSO) 算法的调度算法,灵感来自猫的行为.
- 在使用实时工作负载的CloudSim和OpenStack平台上实现并测试了算法.
主要成果:
- 拟议的基于猫群优化的调度算法与基线算法 (PSO,ACO,RATS-HM) 相比,表现优越.
- 观察到显著的改善是尽量减少制造量,减少能源消耗,减少SLA违规.
- 该算法有效地根据计算的优先级将任务与适当的虚拟机 (VM) 相匹配.
结论:
- 使用猫群优化的新型任务调度算法为云资源管理提供了更有效的方法.
- 提出的方法成功地解决了关键绩效指标:制造量,能源消耗和SLA违规.
- 这项研究为优化云调度提供了宝贵的贡献,以提高效率和服务质量.
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