High-speed algorithm for density of parallel tasks in cloud computing load testing

doi:10.11772/j.issn.1001-9081.2014.07.1839

Journal of Computer Applications ›› 2014, Vol. 34 ›› Issue (7): 1839-1842.DOI: 10.11772/j.issn.1001-9081.2014.07.1839

High-speed algorithm for density of parallel tasks in cloud computing load testing

BAI Yu,GUO Xiane

School of Mathematics and Computer Science, Shanxi Datong University, Datong Shanxi 037009, China

Received:2014-01-02 Revised:2014-02-17 Online:2014-08-01 Published:2014-07-01
Contact: BAI Yu

求解云计算压力测试中并行任务密度的高速算法

白宇,郭显娥

山西大同大学数学与计算机科学学院，山西大同 037009

通讯作者: 白宇
作者简介:白宇(1976-)，男，山西大同人，讲师，硕士，主要研究方向：图论算法、并行算法、云计算；郭显娥(1964-)，女，山西大同人，教授，主要研究方向：概念格、数据挖掘、知识发现。
基金资助:
教育部高等学校计算机课程改革项目;山西大同大学教研一般项目

Abstract

Abstract:

During the current cloud computing load testing, in order to solve the problem of low efficiency of the algorithm for calculating the density of parallel task to the data collected, based on the idea of space for time, using the method of mathematical analysis, a high-speed algorithm for density of parallel task was proposed. Its time complexity is O(n lb n) and the space complexity is O(n). The experimental results show that, compared with the OpenSTA algorithm with the same time complexity, the increment of efficiency is about 6 to 8 times. The algorithm of multiple the same density of parallel tasks can obtain maximum duration, which can accurately reflect the situation of the heaviest load. The algorithm is applied to obtain the real reference data for the design of cloud computing load balancing algorithm.

摘要：

针对当前云计算负载压力测试过程中，对所采集数据计算并行任务密度的算法效率较低的问题，基于空间换时间的思路，使用数学分析的方法，提出了一种时间复杂度为O(n lb n)，空间复杂度为O(n)的求解并行任务密度的高速算法。实验结果表明，该算法与时间复杂度同为O(n lb n)的OpenSTA算法相比，效率约有6~8倍的提升。该算法对多个相同的并行任务密度能够解得并行时长最长者，可以准确反映负载最重的情况。该算法适合云计算进行负载均衡算法设计时，获取真实参照数据使用。

CLC Number:

TP311.12

BAI Yu GUO Xiane. High-speed algorithm for density of parallel tasks in cloud computing load testing[J]. Journal of Computer Applications, 2014, 34(7): 1839-1842.

白宇郭显娥. 求解云计算压力测试中并行任务密度的高速算法[J]. 计算机应用, 2014, 34(7): 1839-1842.

[1]	Danfeng ZHAO, Junchen LIN, Wei SONG, Jian WANG, Dongmei HUANG. Strategy with low redundant computation for reachability query preserving graph compression [J]. Journal of Computer Applications, 2020, 40(2): 510-517.
[2]	JIANG Shunliang, HU Shihong, TANG Yiling, GE Yun, YE Famao, XU Shaoping. Generalized AVL tree with low adjusting ratio and its unified rebalancing method [J]. Journal of Computer Applications, 2015, 35(3): 654-658.
[3]	GUO Dong WANG Wei ZENG Guoshun. Distributed data storage method based on consistent tree distribution [J]. Journal of Computer Applications, 2013, 33(12): 3432-3436.
[4]	BAI Yu GUO Xiane. Lightweight evaluation algorithm for infix arithmetic expression [J]. Journal of Computer Applications, 2013, 33(11): 3163-3166.
[5]	BAI Yu GUO Xiane. Efficient algorithm of depth-first stable in-place merge sort [J]. Journal of Computer Applications, 2013, 33(04): 1039-1042.
[6]	LI Wen-chuang ZHANG Yong-ping PAN Yu-chun. Multi-source heterogeneous data fusion model in mobile geographic information system [J]. Journal of Computer Applications, 2012, 32(09): 2672-2674.
[7]	LING Xu-xiong WANG She-guo LI Yang MIAO Zai-liang. No-header-table FP-Growth algorithm [J]. Journal of Computer Applications, 2011, 31(05): 1391-1394.
[8]	ZHANG Tao，YU Xue-qin. Storage strategy of spatial metadata based on XML data reduced schema [J]. Journal of Computer Applications, 2005, 25(07): 1590-1591.
[9]	ZHOU Yong, HE Jian-nong, TU Ping. Technique of auto-selection multi-layers grid spatial index [J]. Journal of Computer Applications, 2005, 25(06): 1401-1404.
[10]	WEI Hong-ning. Study on the parallelism of decision tree classification based on SPRINT [J]. Journal of Computer Applications, 2005, 25(01): 39-41.

High-speed algorithm for density of parallel tasks in cloud computing load testing

求解云计算压力测试中并行任务密度的高速算法

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 10

Recommended Articles

Metrics