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首页 > 过刊浏览>2018年第29卷第11期 >3326-3339. DOI:10.13328/j.cnki.jos.005477
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云环境下基于多目标的多科学工作流调度算法
作者:
作者简介:

袁友伟(1966-),男,湖北潜江人,博士,教授,CCF专业会员,主要研究领域为云计算,工作流调度;俞东进(1969-),男,博士,教授,博士生导师,CCF高级会员,主要研究领域为软件工程理论和方法,业务过程管理,行业大数据;鲍泽前(1994-),男,硕士生,主要研究领域为工作流调度,大数据分析;李万清(1979-),男,博士,副教授,主要研究领域为大数据分析.

通讯作者:

鲍泽前,E-mail:549135624@qq.com

基金项目:

国家自然科学基金（61370218）；浙江省重点高校建设专项资金（GK158800205032）


Multi-Scientific Workflow Scheduling Algorithm Based on Multi-Objective in Cloud Environment
Author:
Fund Project:

National Natural Science Foundation of China (61370218); Key University Construction Project of Zhejiang Province of China (GK158800205032)

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    摘要:

    针对现有云环境下的多科学工作流调度算法中存在的未考虑安全调度问题，提出了多科学工作流安全-时间约束费用优化算法MSW-SDCOA（multi-scientific workflows security-deadline constraint cost optimizationalgorithm）.首先，MSW-SDCOA基于数据依赖关系压缩科学工作流，减少任务节点数从而节省了调度开销；并通过改进HEFT（heterogeneous earliest-finish-time）算法形成调度序列，以实现全局多目标优化调度；最后，通过优化ACO（antcolony optimization）中信息素更新策略和启发式信息，进一步改善费用优化效果.仿真实验表明，MSW-SDCOA算法在费用优化效果上比MW-DBS算法提高了约14%.

    Abstract:

    To address the problem that safe scheduling is not taken into consideration in existing multi-scientific scheduling workflow algorithm in cloud environment, this paper proposes a multi-scientific workflows security-deadline constraint cost optimization algorithm (MSW-SDCOA). First, based on data flow dependency, MSW-SDCOA compresses scientific workflow and reduces the number of task nodes to save scheduling cost. Secondly, through optimizing HEFT algorithm, a scheduling sequence is formed to realize overall multi-objective optimization scheduling. Lastly, by optimizing update strategies of pheromone and heuristic information in ant colony optimization (ACO), cost optimization effect is further improved. The simulation experiment results show that the cost optimization effect of MSW-SDCOA algorithm is about 14% better than that of MW-DBS algorithm.

    参考文献
    [1] Deelman E, Singh G, Livny M, Berriman B. The cost of doing science on the cloud:The montage example. In:Proc. of the 2008 ACM/IEEE Conf. on Supercomputing. Piscataway:IEEE, 2008. 1-12.
    [2] Kannas CC, Kalvari I, Lambrinidis G, Neophytou CM, Savva CG, Kirmitzoglou I, Antoniou Z, Achilleos KG, Scherf D, Pitta CA. LiSIs:An online scientific workflow system for virtual screening. Combinatorial Chemistry & High Throughput Screening, 2015, 18(3):281-295.
    [3] Bennett JC, Bhagatwala A, Chen JH, Seshadhri C, Pinar A, Salloum M. Trigger detection for adaptive scientific workflows using percentile sampling. SIAM Journal on Scientific Computing, 2016,38(5):S240-S263.
    [4] Pradal C, Artzet S, Chopard J, Dupuis D, Fournier C, Mielewczik M, Nègre V, Neveu P, Parigot D, Valduriez P. InfraPhenoGrid:A scientific workflow infrastructure for plant phenomics on the grid. Future Generation Computer Systems, 2017,67:341-353.
    [5] Zhao Y, Li Y, Raicu I, Lu S, Lin C, Zhang Y, Tian W, Xue R. A service framework for scientific workflow management in the cloud. IEEE Trans. on Services Computing, 2015,8(6):930-944.
    [6] Wang Y, Jia C, Xu Y. Multiple dags dynamic workflow scheduling based on the primary backup algorithm in cloud computing system. In:Proc. of the Ninth Int'l Conf. on Broadband and Wireless Computing, Communication and Applications. Piscataway:IEEE, 2014. 177-182.
    [7] Tischer A, Auton M. Scheduling strategies for mapping application workflows onto the grid. In:Proc. of the 14th IEEE Int'l Symp. on High Performance Distributed Computing. Piscataway:IEEE, 2005. 125-134.
    [8] Zhao H, Sakellariou R. Scheduling multiple DAGs onto heterogeneous systems. In:Proc. of the 2006 Parallel and Distributed Processing Symp. Washington:IEEE, 2006. 1-14.
    [9] Rodriguez MA, Buyy R. Deadline based resource provisioning and scheduling algorithm for scientific workflows on clouds. IEEE Trans. on Cloud Computing, 2014,2(2):222-235.
    [10] Weins K. Cloud computing trends:2017 state of the cloud survey. 2017. https://www.rightscale.com/blog/cloud-industryinsights/cloud-computing-trends-2017-state-cloud-survey
    [11] Yurcik W, Koenig GA, Meng X, Greenseid J. Cluster security as a unique problem with emergent properties:Issues and techniques. In:Proc. of the 5th Lci Int'l Conf. on Linux Clusters:The Hpc Revolution. 2004. 18-20.
    [12] Arabnejad H, Barbosa JG, Prodan R. Low-Time complexity budget-deadline constrained workflow scheduling on heterogeneous resources. Future Generation Computer Systems, 2016,55:29-40.
    [13] Lin B, Guo W, Chen G, Xiong N, Li R. Cost-Driven scheduling for deadline-constrained workflow on multi-clouds. In:Proc. of the 2015 IEEE Int'l Parallel and Distributed Processing Symp. Workshop. Piscataway:IEEE, 2015. 1191-1198.
    [14] Meena J, Kumar M, Vardhan M. Cost effective genetic algorithm for workflow scheduling in cloud under deadline constraint. IEEE Access, 2016,4:5065-5082.
    [15] Singh L, Singh S. Deadline and cost based ant colony optimization algorithm for scheduling workflow applications in hybrid cloud. Journal of Scientific & Engineering Research, 2014,5(10):1417-1420.
    [16] Verma A, Kaushal S. A hybrid multi-objective particle swarm optimization for scientific workflow scheduling. Parallel Computing, 2017,62:1-19.
    [17] Zissis D, Lekkas D. Addressing cloud computing security issues. Future Generation Computer Systems, 2012,28(3):583-592.
    [18] Huang Q, Yang Y, Shen M. Secure and efficient data collaboration with hierarchical attribute-based encryption in cloud computing. Future Generation Computer Systems, 2017,72:239-249.
    [19] Sookhak M, Yu FR, Khan MK, Xiang Y, Buyya R. Attribute-Based data access control in mobile cloud computing:Taxonomy and open issues. Future Generation Computer Systems, 2017,72:273-287.
    [20] Ali M, Khan SU, Vasilakos AV. Security in cloud computing:Opportunities and challenges. Information Sciences, 2015,305:357-383.
    [21] Sharif S, Taheri J, Zomaya AY, Nepal S. Mphc:Preserving privacy for workflow execution in hybrid clouds. In:Proc. of the 2013 Int'l Conf. on Parallel and Distributed Computing, Applications and Technologies. Piscataway:IEEE, 2013. 272-280.
    [22] Zeng L, Veeravalli B, Li X. SABA:A security-aware and budget-aware workflow scheduling strategy in clouds. Journal of Parallel and Distributed Computing, 2015,75:141-151.
    [23] Bala A, Chana I. Intelligent failure prediction models for scientific workflows. Expert Systems with Applications, 2015,42(3):980-989.
    [24] Topcuoglu H, Hariri S, Wu M. Performance-Effective and low-complexity task scheduling for heterogeneous computing. IEEE Trans. on Parallel and Distributed Systems, 2002,13(3):260-274.
    [25] Xie T, Qin X. Scheduling security-critical real-time applications on clusters. IEEE Trans. on Computers, 2006,55(7):864-879.
    [26] Tang XY, Li K, Zeng Z, Veeravalli B. A novel security-driven scheduling algorithm for precedence-constrained tasks in heterogeneous distributed systems. IEEE Trans. on Computers, 2011,60(7):1017-1029.
    [27] Arabnejad H, Barbosa JG. Multi-Workflow QoS-constrained scheduling for utility computing. In:Proc. of the 2015 IEEE 18th Int'l Conf. on Computational Science and Engineering. Piscataway:IEEE, 2015. 137-144.
    [28] Calheiros RN, Ranjan R, Beloglazov A, Rose CAFD, Buyya R. CloudSim:A toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software:Practice and Experience, 2011,41(1):23-50.
    [29] Bharathi S, Chervenak A, Deelman E, Mehta G, Su MH, Vahi K. Characterization of scientific workflows. In:Proc. of the 3rd Workshop on Workflows in Support of Large-Scale Science. Piscataway:IEEE, 2008. 1-10.
    [30] Vinay VP, Sridharn R. Taguchi method for parameter design in ACO algorithm for distribution-allocation in a two-stage supply chain. Int'l Journal of Advanced Manufacturing Technology, 2013,64(9-12):1333-1343.
    [31] Siemiński A. Ant colony optimization parameter evaluation. Multimedia and Internet Systems:Theory and Practice, 2013,183:143-153.
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袁友伟,鲍泽前,俞东进,李万清.云环境下基于多目标的多科学工作流调度算法.软件学报,2018,29(11):3326-3339

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  • 收稿日期:2017-07-19
  • 最后修改日期:2017-09-16
  • 录用日期:2017-11-14
  • 在线发布日期: 2017-12-05
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