An adaptive algorithm for scheduling parallel jobs in meteorological Cloud
An adaptive algorithm for scheduling parallel jobs in meteorological Cloud
复制标题
气象云中并行作业调度的自适应算法
DOI：
10.1016/j.knosys.2016.01.038
复制
发表时间：
2016-04
影响因子：
8.8
通讯作者：
Zheng Mai
中科院分区：
文献类型：
--
作者：
Hao Yongsheng;Wang Lina;Zheng Mai
从传统集群到云系统，作业调度是在任何分布式环境中实现高性能的最关键因素之一。在本文中，我们提出了一种在气象云中调度模块化非线性并行作业的自适应算法，该算法具有独特的并行性，只能在执行的最开始进行配置。与现有工作不同，我们的算法同时考虑了作业的四个特征，包括平均执行时间、作业的截止日期、分配的资源数量和整体系统负载。我们通过使用科学计算中广泛使用的 WRF（天气研究和预报模型）进行模拟来证明我们的调度算法的有效性和效率。我们的评估结果表明，与之前的方法相比，该算法具有多种优势，包括执行时间减少了 10% 以上、满足软截止日期方面的完成率更高、平均加权执行时间的标准偏差更小。此外，我们表明所提出的算法可以容忍系统负载估计的不准确性。
From traditional clusters to cloud systems, job scheduling is one of the most critical factors for achieving high performance in any distributed environment. In this paper, we propose an adaptive algorithm for scheduling modular non-linear parallel jobs in meteorological Cloud, which has a unique parallelism that can only be configured at the very beginning of the execution. Different from existing work, our algorithm takes into account four characteristics of the jobs at the same time, including the average execution time, the deadlines of jobs, the number of assigned resources, and the overall system loads. We demonstrate the effectiveness and efficiency of our scheduling algorithm through simulations using WRF (Weather Research and Forecasting model) that which is widely used in scientific computing. Our evaluation results show that the proposed algorithm has multiple advantages compared with previous methods, including more than 10% reduction in terms of execution time, a higher completion ratio in terms of meeting soft deadlines, and a much smaller standard deviation of the average weighted execution time. Moreover, we show that the proposed algorithm can tolerate inaccuracy in system load estimation.
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DOI：
10.1109/rtas.2013.6531098
发表时间：
2013-04
期刊：
2013 IEEE 19th Real-Time and Embedded Technology and Applications Symposium (RTAS)
影响因子：
--
作者：
D. Ferry;Jing Li;M. Mahadevan;Kunal Agrawal;C. Gill;Chenyang Lu
通讯作者：
D. Ferry;Jing Li;M. Mahadevan;Kunal Agrawal;C. Gill;Chenyang Lu
DOI：
10.1109/ccdc.2013.6561332
发表时间：
2013-05
期刊：
2013 25th Chinese Control and Decision Conference (CCDC)
影响因子：
--
作者：
Ming Tao;Shoubin Dong
通讯作者：
Ming Tao;Shoubin Dong
影响因子：
3.7
作者：
Zong, Ziliang;Manzanares, Adam;Qin, Xiao
通讯作者：
Qin, Xiao
DOI：
10.1109/ccgrid.2013.73
发表时间：
2013-05
期刊：
2013 13th IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing
影响因子：
--
作者：
Jing Jiang;Jie Lu;Guangquan Zhang;Guodong Long
通讯作者：
Jing Jiang;Jie Lu;Guangquan Zhang;Guodong Long
DOI：
--
发表时间：
2011-10
期刊：
2011 7th International Conference on Network and Service Management
影响因子：
--
作者：
S. Dutta;Akshat Verma
通讯作者：
S. Dutta;Akshat Verma