Two-step task scheduling strategy for scientific workflow on cloud computing platform

doi:10.3724/SP.J.1087.2013.01006

Journal of Computer Applications ›› 2013, Vol. 33 ›› Issue (04): 1006-1009.DOI: 10.3724/SP.J.1087.2013.01006

• Network and distributed techno • Previous Articles Next Articles

Two-step task scheduling strategy for scientific workflow on cloud computing platform

YAN Ge,YU Jiong,YANG Xingyao

School of Information Science and Engineering, Xinjiang University, Urumqi Xingjiang 830046, China

Received:2012-10-26 Revised:2012-11-22 Online:2013-04-23 Published:2013-04-01
Contact: YAN Ge

云计算环境下科学工作流两阶段任务调度策略

闫歌,于炯,杨兴耀

新疆大学信息科学与工程学院，乌鲁木齐 830046

通讯作者: 闫歌
作者简介:闫歌 (1988-)，女，新疆乌鲁木齐人，硕士研究生，主要研究方向：云计算、网格与分布式计算；于炯(1964-)，男，新疆乌鲁木齐人，教授，博士，主要研究方向：网络安全、网格与分布式计算；杨兴耀(1984－)，男，新疆乌鲁木齐人，博士研究生，主要研究方向：数据库、网格与分布式计算。
基金资助:
国家自然科学基金资助项目(60863003，61063042，61262088);新疆维吾尔自治区自然科学基金资助项目(2011211A011)

Abstract

Abstract: According to the research and analysis on the existing task scheduling strategy of scientific workflow under the cloud environment, a two-step task scheduling strategy was proposed. This strategy aimed at solving or alleviating the phenomenon of resource idle in Heterogeneous Earliest Finish Time (HEFT) algorithm and SHEFT algorithm. Along with the characteristics of cloud computing environment, it derives from the SHEFT algorithm. It can make the most use of the resources idle time and get the minimum makespan. The experiments and performance analysis for the scheduling strategy show that it has a significant improvement in the workflow makespan and resource utilization.

Key words: cloud computing, scientific workflow, Heterogeneous Earliest Finish Time (HEFT) algorithm, SHEFT algorithm, resource allocation, task scheduling

摘要： 经过对云环境下科学工作流现有的任务调度策略进行分析研究，针对异态最早结束时间(HEFT)算法及其改进算法SHEFT在任务执行过程中出现的资源闲置现象，结合云计算的特点，在SHEFT算法的基础上提出了一种两阶段任务调度策略。该策略在完成时间最少的情况下能够对资源的闲置时间进行尽可能的利用。经过对该算法进行实验和性能分析，表明该策略在完成时间和资源利用方面都有很大改进。

关键词: 云计算, 科学工作流, 异态最早结束时间算法, SHEFT算法, 资源分配, 任务调度

CLC Number:

TP301.6

YAN Ge YU Jiong YANG Xingyao. Two-step task scheduling strategy for scientific workflow on cloud computing platform[J]. Journal of Computer Applications, 2013, 33(04): 1006-1009.

闫歌于炯杨兴耀. 云计算环境下科学工作流两阶段任务调度策略[J]. 计算机应用, 2013, 33(04): 1006-1009.

References

［1］BORNE K D．Data-driven discovery through e-science technologies［C］// Proceedings of the 2nd IEEE International Conference on Space Mission Challenges for Information Technology．Washington，DC，USA：IEEE Computer Society，2006：251-256．

［2］LIN C，LU S Y，FEI X B，et al.A reference architecture for scientific workflow management systems and the view SOA solution［J］．IEEE Transactions on Service Computing，2009，2(1)：79-92．

［3］REN K J，CHEN J J，XIAO N．Building Quick Service Query List (QSQL) to support automated service discovery for scientific workflow［J］．Concurrency and Computation Practice and Experience，2009，21(16)：2099-2117．

［4］WEISS A．Computing in the cloud［J］．netWorker, 2007，11(4)：18-25．

［5］DEELMAN E，SINGH G，LIB-NY M，et al.The cost of doing science on the cloud: the Montage example［C］// Proceedings of the 2008 ACM/IEEE Conference on Supercomputing．Piscataway：IEEE Press，2008：1-12．

［6］EKANAYAKE J X, QIU T, GUNARATHNE S, et al.High performance parallel computing with clouds and cloud technologies［M］// Cloud Computing and Software Services：Theory and Techniques. [S.l.]: CRC Press，2010: 20-38．

［7］HWANG K，DONGARRA J，FOX C C．Distributed and cloud computing systems: from parallel processing to the Internet of things［M］.Grids，Clouds，and the Future Internet. [S.l.]: Morgan Kauffmann，2011．

［8］MORETTI C，BUI H，HOLLINGSWORTH K，et al.An abstraction for data-intensive computing on campus grids［J］．IEEE Transactions on Parallel and Distributed Systems，2010，21(1)：33-46．

［9］郭文彩，杨扬．基于遗传算法的网格服务工作流调度的研究［J］．计算机应用，2006,26(1)：54-56．

［10］王勇，胡春明，杜宗霞．服务质量感知的网格上作流调度［J］．软件学报，2006，17(11)：2341-2351．

［11］郭文彩，杨扬．一种面向服务的网格工作流调度算法［J］．计算机科学，2006，33(6)：132-134．

［12］PRODAN R， FAHRINGER T． Dynamic scheduling of scientific workflow applications on the grid using a modular optimisation tool: a case study［C］// 20th Symposion of Applied Computing．Santa Fe，New Mexico，USA：ACM，2005：56-62．

［13］ZHAO H N，SAKELLARIOU R．An experimental investigation into the rank function of the heterogeneous earliest finish time scheduling algorithm［J］．Euro-Par - European Conference on Parallel Processing, LNCS 2790. Berlin: Springer，2003：189-194．

［14］DECKER J，SCHNEIDER J．Heuristic scheduling of grid workflows supporting co-allocation and advance reservation［C］// Seventh IEEE International Symposium on Cluster Computing and the Grid．Washington, DC：IEEE Computer Society，2007：335-342．

［15］WIECZOREK M，SIDDIQUI M．Applying advance reservation to increase predictability of workflow execution on the grid［C］// Second IEEE International Conference on E-Science and Grid Computing．Amsterdam, The Netherlands：IEEE，2006：82．

［16］CUI L．Scientific workflow integration for services computing［D］．Detroit：Wayne State University, 2010

[1]	Junna ZHANG, Xinxin WANG, Tianze LI, Xiaoyan ZHAO, Peiyan YUAN. Task offloading method based on dynamic service cache assistance [J]. Journal of Computer Applications, 2024, 44(5): 1493-1500.
[2]	Rui TANG, Chuanlin PANG, Ruizhi ZHANG, Chuan LIU, Shibo YUE. DDPG-based resource allocation in D2D communication-empowered cellular network [J]. Journal of Computer Applications, 2024, 44(5): 1562-1569.
[3]	Hualiang LUO, Quanzhong LI, Qi ZHANG. Robust resource allocation optimization in cognitive wireless network integrating information communication and over-the-air computation [J]. Journal of Computer Applications, 2024, 44(4): 1195-1202.
[4]	Fatang CHEN, Miao HUANG, Yufeng JIN. Resource allocation algorithm for low earth orbit satellites oriented to user demand [J]. Journal of Computer Applications, 2024, 44(4): 1242-1247.
[5]	Rui TANG, Shibo YUE, Ruizhi ZHANG, Chuan LIU, Chuanlin PANG. Energy efficiency optimization mechanism for UAV-assisted and non-orthogonal multiple access-enabled data collection system [J]. Journal of Computer Applications, 2024, 44(4): 1209-1218.
[6]	Xintong QIN, Zhengyu SONG, Tianwei HOU, Feiyue WANG, Xin SUN, Wei LI. Channel access and resource allocation algorithm for adaptive p-persistent mobile ad hoc network [J]. Journal of Computer Applications, 2024, 44(3): 863-868.
[7]	Yongjian MA, Xuhua SHI, Peiyao WANG. Constrained multi-objective evolutionary algorithm based on two-stage search and dynamic resource allocation [J]. Journal of Computer Applications, 2024, 44(1): 269-277.
[8]	Shaofa SHANG, Lin JIANG, Yuancheng LI, Yun ZHU. Adaptive partitioning and scheduling method of convolutional neural network inference model on heterogeneous platforms [J]. Journal of Computer Applications, 2023, 43(9): 2828-2835.
[9]	Jiaxing LU, Hua DAI, Yuanlong LIU, Qian ZHOU, Geng YANG. Dictionary partition vector space model for ciphertext ranked search in cloud environment [J]. Journal of Computer Applications, 2023, 43(7): 1994-2000.
[10]	Lei LI, Guofu ZHANG, Zhaopin SU, Feng YUE. Software testing resource allocation algorithm for dynamic changes in architecture [J]. Journal of Computer Applications, 2023, 43(7): 2261-2270.
[11]	Heping FANG, Shuguang LIU, Yongyi RAN, Kunhua ZHONG. Integrated scheduling optimization of multiple data centers based on deep reinforcement learning [J]. Journal of Computer Applications, 2023, 43(6): 1884-1892.
[12]	Mingchao NING, Junbo ZHANG, Ge CHEN. Task scheduling algorithm for service-oriented architecture-based industrial software [J]. Journal of Computer Applications, 2023, 43(3): 885-893.
[13]	Qian LIU, Yangming ZHANG, Dingsheng WAN. Parallel computing algorithm of grid-based distributed Xin’anjiang hydrological model [J]. Journal of Computer Applications, 2023, 43(11): 3327-3333.
[14]	Kun YOU, Qinhui WANG, Xin LI. General multi-unit false-name-proof auction mechanism for cloud computing [J]. Journal of Computer Applications, 2023, 43(11): 3351-3357.
[15]	WU Renbiao, ZHANG Zhenchi, JIA Yunfei, QIAO Han. Adaptive scheduling strategy based on deadline under cloud platform [J]. Journal of Computer Applications, 2023, 43(1): 176-184.

Two-step task scheduling strategy for scientific workflow on cloud computing platform

云计算环境下科学工作流两阶段任务调度策略

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics