[1]段菊,于治国.云环境下基于相关性的并行任务调度策略[J].计算机技术与发展,2018,28(06):178-183.[doi:10.3969/ j. issn.1673-629X.2018.06.040]
　DUAN Ju,YU Zhi-guo.Parallel Tasks Scheduling Strategy Based on Correlation under Cloud Environment[J].,2018,28(06):178-183.[doi:10.3969/ j. issn.1673-629X.2018.06.040]
点击复制

云环境下基于相关性的并行任务调度策略()
分享到：

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
28
期数:
2018年06期
页码:
178-183
栏目:
应用开发研究
出版日期:
2018-06-10

文章信息/Info

Title:
Parallel Tasks Scheduling Strategy Based on Correlation under Cloud Environment
文章编号:
1673-629X(2018)06-0178-06
作者:
段菊; 于治国
山东管理学院 信息化工作办公室,山东 济南 250357
Author(s):
DUAN Ju; YU Zhi-guo
Informatization Office,Shandong Management University,Jinan 250357,China
关键词:
相关性; 通信开销; 阈值; 任务复制; 任务调度
Keywords:
correlation; communication overhead; threshold value; task replication; task scheduling
分类号:
TP301.6
DOI:
10.3969/ j. issn.1673-629X.2018.06.040
文献标志码:
A
摘要:
为了提高云环境下任务的执行效率、降低执行费用,提出了一种基于相关性的并行任务调度策略。该策略在任务调度之前根据任务间的通信开销进行队列划分,通过队列的划分可以缩短最晚路径的完成时间,然后根据相关性进行任务复制,任务复制算法降低了任务的等待时间,提高了任务的并行性。经过任务复制,每个处理机上的任务队列基本都是相互独立的,提高了任务的执行效率。 相关性由任务间的通信开销和计算开销来量化并设定阈值,若相关性大于阈值则进行任务复制,否则不予复制。该策略既可以减少由任务间的通信带来的开销,也可以避免由所有任务复制带来的空间消耗。 实验结果表明,该方法可以提高任务的并行度,在提高任务的执行效率及降低执行费用方面有很大的改进。
Abstract:
In order to improve the execution efficiency and lower execution cost of the tasks in cloud,we propose a policy of parallel tasks scheduling based on correlation. The queues are divided according to the communication overhead between tasks before the tasks are scheduled. The finish time of the latest path can be shortened by the partition of the queues. Then,the task is replicated according to the correlation. Task replication algorithm can reduce the waiting time of tasks and improve the parallelism of tasks. After replication,the task queues on the processors are independent of each other,improving the execution efficiency of the tasks. Correlation is quantified by the communication overhead and computational overhead between tasks and set threshold value. If the correlation is greater than the threshold value,then task is replicated,otherwise not be replicated. This strategy can reduce the communication overhead between tasks and avoid the space consumption caused by all tasks replication. Experiment shows that the strategy can improve the parallelism of the tasks and it has a significant effect in improving the utilization ratio of processors and lowering the cost of tasks execution.

相似文献/References:

[1]张家柏 王小玲.基于聚类和二进制PSO的特征选择[J].计算机技术与发展,2010,(06):25.
　ZHANG Jia-bai,WANG Xiao-ling.A Novel Algorithm Based on K-Means Clustering and Binary Particle Swarm Optimization[J].,2010,(06):25.
[2]刘忠艳 周波 车向前.一种高效的图像匹配算法[J].计算机技术与发展,2009,(04):45.
　LIU Zhong-yan,ZHOU Bo,CHE Xiang-qian.An Effective Algorithm for Image Registration[J].,2009,(06):45.
[3]郝其伟 明军.基于KL变换的烟叶信号阈值优化方法[J].计算机技术与发展,2007,(05):8.
　HAO Qi-wei,MING Jun.Optimum Method about Threshold of Tobacco Leaf Signals Based on KL Transform[J].,2007,(06):8.
[4]李泽军 曾利军 刘文华.基于相关性和语义相似度融合的查询扩展方法[J].计算机技术与发展,2010,(09):66.
　LI Ze-jun,ZENG Li-jun,LIU Wen-hua.Query Expansion Method Based on Relativity and Similarity Inosculate[J].,2010,(06):66.
[5]方承志 刘洁 王坦.无线局域网中基于响应相关性的Turbo译码研究[J].计算机技术与发展,2012,(12):215.
　FANG Cheng-zhi,LIU Jie,WANG Tan.Research on Turbo Decoding Based on Response Correlation in WLAN[J].,2012,(06):215.
[6]张子敬,霍家道.基于 H.264的帧内自适应预测算法[J].计算机技术与发展,2013,(01):83.
　ZHANG Zi-jing,HUO Jia-dao.An Adaptive Prediction Intra-frame Algorithm Based on H. 264[J].,2013,(06):83.
[7]谭文安[][],查安民[],陈森博[]. 优化粒子群的云计算任务调度算法[J].计算机技术与发展,2016,26(07):6.
　TAN Wen-an[]],ZHA An-min[],CHEN Sen-bo[]. Task Scheduling Algorithm of Cloud Computing Based on Particle Swarm Optimization [J].,2016,26(06):6.
[8]胡荣辉,王瑞通. 云环境下虚拟机集群迁移策略研究[J].计算机技术与发展,2017,27(11):33.
　HU Rong-hui,WANG Rui-tong. Study on Virtual Cluster Migration Strategy in Cloud Environment[J].,2017,27(06):33.
[9]于文静,余洁,徐凌宇.海表面温时间序列的相关性及复杂性研究[J].计算机技术与发展,2019,29(02):181.[doi:10．3969/j．issn．1673－629X．2019．02．038]
　YU Wenjing,YU Jie,XU Lingyu.Research on Correlation and Complexity of Sea Surface Temperature Time Series[J].,2019,29(06):181.[doi:10．3969/j．issn．1673－629X．2019．02．038]
[10]杨井荣,侯向宁.正负关联规则数据挖掘算法研究[J].计算机技术与发展,2020,30(11):64.[doi:10. 3969 / j. issn. 1673-629X. 2020. 11. 012]
　YANG Jing-rong,HOU Xiang-ning.Research on Data Mining Algorithm Based on Positive and Negative Association Rules[J].,2020,30(06):64.[doi:10. 3969 / j. issn. 1673-629X. 2020. 11. 012]

更新日期/Last Update: 2018-08-22