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论文:2024,Vol:42,Issue(2):319-327 |
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引用本文： |
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刘明轩, 郭博渊, 刘曦, 梁欣欣, 赵强龙, 杨晓峰, 谷建华. 一种用于星载虚拟化平台的任务容器调度算法[J]. 西北工业大学学报 |
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LIU Mingxuan, GUO Boyuan, LIU Xi, LIANG Xinxin, ZHAO Qianglong, YANG Xiaofeng, GU Jianhua. A task container scheduling algorithm for spaceborne virtualization platform[J]. Journal of Northwestern Polytechnical University |
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| 一种用于星载虚拟化平台的任务容器调度算法 |
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| 刘明轩1, 郭博渊2, 刘曦2, 梁欣欣2, 赵强龙1, 杨晓峰1, 谷建华1 |
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1. 西北工业大学 计算机学院, 陕西 西安 710072;
2. 西安微电子技术研究所, 陕西 西安 710065 |
| 摘要: |
| 星载虚拟化平台借助容器等轻量级虚拟化技术,将计算任务封装到容器中形成任务容器,从而实现资源的高效利用。然而,该平台的任务容器调度问题是一个亟需解决的难题。针对这一问题建立了一个基于非阻塞通信模式的可分容器任务多趟调度模型。在该基础上,提出了一种新的调度算法,旨在确定最佳的处理机调度顺序和调度趟数。该算法结合可分任务容器和多趟调度的概念,通过将任务分解为可执行的子任务,并在多个调度阶段中进行任务分配和处理机调度,从而优化调度顺序,提高整体处理效率。该算法是一种改进的遗传算法,在传统遗传算法的基础上添加了子种群隔离的优化策略,其核心思想是将种群划分策略引入算法过程,从而改善遗传算法的性能和效果。通过实验验证了该算法的有效性和收敛性,结果表明,该算法缩短了任务完成时间。 |
| 关键词:
星载计算
虚拟化
容器
可分任务调度
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| A task container scheduling algorithm for spaceborne virtualization platform |
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| LIU Mingxuan1, GUO Boyuan2, LIU Xi2, LIANG Xinxin2, ZHAO Qianglong1, YANG Xiaofeng1, GU Jianhua1 |
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1. School of Computer Science, Northwestern Polytechnical University, Xi'an 710072, China;
2. Xi'an Institute of Microelectronics Technology, Xi'an 710065, China |
| Abstract: |
| With the help of lightweight virtualization technology such as containers, the spaceborne virtualization platform encapsulates computing tasks into containers to form task containers, so as to achieve efficient utilization of resources. However, the platform's task container scheduling problem is a difficult problem that needs to be solved urgently. In this paper, we aim at this problem by establishing a multi-pass scheduling model for separable container tasks based on non-blocking communication mode. On the basis of this model, we propose a new scheduling algorithm, aiming at determining the optimal processor scheduling sequence and scheduling times. The algorithm combines the concept of divisible task container and multi-pass scheduling. By decomposing the task into executable subtasks, and performing task allocation and processor scheduling in multiple scheduling stages, it can optimize the scheduling order to improve the overall processing efficiency. This algorithm is an improved genetic algorithm that adds the optimization strategy of subpopulation isolation to the traditional genetic algorithm. Its core idea is to improve the performance and effect of the genetic algorithm by introducing the population division strategy into the algorithm process. We verify the effectiveness and convergence of the algorithm through experiments, and the experimental results show that the algorithm makes the task have less completion time. |
| Key words:
spaceborne computing
virtualization
container
separable task scheduling
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| 收稿日期: 2023-02-23
修回日期:
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| DOI: 10.1051/jnwpu/20244220319 |
| 基金项目: 陕西省重点研发计划(2023-ZDLGY-08)资助 |
| 通讯作者: 谷建华(1965—),教授 e-mail:gujh@nwpu.edu.cn
Email：gujh@nwpu.edu.cn |
| 作者简介: 刘明轩(1997—),博士研究生
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参考文献: |
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[1] KAZEMI M, GHANBARI S, KAZEMI M. Divisible load framework and close form for scheduling in fog computing systems[C]//Proceedings of the Fourth International Conference on Soft Computing and Data Mining, Melaka, Malaysia, 2020: 22-23
[2] NIKBAKHT AALI S, BAGHERZADEH N. Divisible load scheduling of image processing applications on the heterogeneous star and tree networks using a new genetic algorithm[J]. Concurrency and Computation: Practice and Experience, 2020, 32(10): e5498
[3] ROBERTAZZI T G, SHI L, ROBERTAZZI T G, et al. Divisible loads and parallel processing//Networking and Computation: Technology, Modeling and Performance[M]. Springer, 2020: 101-137
[4] SHOKRIPOUR A, OTHMAN M, IBRAHIM H, et al. New method for scheduling heterogeneous multi-installment systems[J]. Future Generation Computer Systems, 2012, 28(8): 1205-1216
[5] BHARADWAJ V, GHOSE D, MANI V. Multi-installment load distribution in tree networks with delays[J]. IEEE Trans on Aerospace and Electronic Systems, 1995, 31(2): 555-567
[6] WANG X, VEERAVALLI B. Performance characterization on handling large-scale partitionable workloads on heterogeneous networked compute platforms[J]. IEEE Trans on Parallel and Distributed Systems, 2017, 28(10): 2925-2938
[7] HAMMERMAN N, GOLDBERG R. Algorithms to improve the convergence of a genetic algorithm with a finite state machine genome//Practical Handbook of Genetic Algorithms[M]. Boca Raton: CRC press, 2019: 119-238
[8] BAI W, REN J, LI T. Modified genetic optimization-based locally weighted learning identification modeling of ship maneuvering with full scale trial[J]. Future generation computer systems, 2019, 93: 1036-1045 |
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