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1.中国科学院大学,北京 100049
2.中国科学院软件研究所基础软件国家工程研究中心,北京 100190
[ "董博南(1987- ),男,北京人,中国科学院大学博士生,主要研究方向为操作系统、计算机架构、系统安全。" ]
[ "杨秋松(1977- ),男,河北沧州人,博士,中国科学院大学教授、博士生导师,主要研究方向为操作系统、软件工程、系统安全。" ]
[ "李明树(1966- ),男,吉林长春人,博士,中国科学院大学教授、博士生导师,主要研究方向为操作系统、软件工程、分布式系统。" ]
收稿日期:2023-10-27,
修回日期:2024-02-19,
纸质出版日期:2024-05-30
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董博南,杨秋松,李明树.M-RSF:面向Unikernel的一种多级反馈队列任务调度机制[J].通信学报,2024,45(05):54-69.
DONG Bonan,YANG Qiusong,LI Mingshu.M-RSF: a multilevel feedback queue task scheduling mechanism for Unikernel[J].Journal on Communications,2024,45(05):54-69.
董博南,杨秋松,李明树.M-RSF:面向Unikernel的一种多级反馈队列任务调度机制[J].通信学报,2024,45(05):54-69. DOI: 10.11959/j.issn.1000-436x.2024061.
DONG Bonan,YANG Qiusong,LI Mingshu.M-RSF: a multilevel feedback queue task scheduling mechanism for Unikernel[J].Journal on Communications,2024,45(05):54-69. DOI: 10.11959/j.issn.1000-436x.2024061.
Unikernel作为云计算领域的前沿技术,具有启动速度快和资源占用少的特点。但是,在云环境大规模任务调度场景下,目前Unikernel缺少根据其任务特点所定制的调度机制,这阻碍了Unikernel进一步发挥其性能优势。针对此问题,首先总结了Unikernel的任务特点。在此基础上,提出了一种新的面向Unikernel的多级反馈队列调度机制M-RSF及其数学模型,在不影响Unikernel结构特点的前提下,能够更有效地对云环境下Unikernel的任务进行调度。同时,基于对任务特性的深入分析,提出了一种新的负载模型,该负载模型不仅可以准确刻画Unikernel的任务特点,还可以为M-RSF中调度策略的参数设置较合理的取值。最后,对M-RSF调度机制在OSv Unikernel上进行了实现和验证,实验结果表明,M-RSF可以有效减少OSv Unikernel在大规模任务调度时的平均等待时间和平均周转时间,相比于未经改动的OSv Unikernel,平均等待时间和平均周转时间减少达15%以上。
Unikernel
as a cutting-edge technology in the field of cloud computing
is characterized by its fast start-up speed and minimal resource usage. However
in the context of large-scale task scheduling in cloud environments
Unikernel currently lacks a customized scheduling mechanism based on its task characteristics
which hinders the further exploitation of its performance advantages. To address this issue
the distinctive task characteristics of Unikernel were initially summarized. Subsequently
a novel multi-level feedback queue scheduling mechanism for Unikernel
referred to as M-RSF
along with its mathematical model
were proposed. Without affecting the structural features of Unikernel
this mechanism could more effectively schedule Unikernel tasks in cloud environments. Meanwhile
a new load model was put forward based on an in-depth analysis of task characteristics. The model not only accurately depicted the task characteristics of Unikernel but also provided a more reasonable value setting for the scheduling policy parameters in M-RSF. Lastly
the implementation and validation of the M-RSF scheduling mechanism on the OSv Unikernel were conducted. Experimental results indicate that M-RSF can effectively reduce the average waiting time and average turnaround time of the OSv Unikernel during large-scale task scheduling
achieving a reduction of more than 15% compared to the unmodified OSv Unikernel.
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