Skip to main content

Advertisement

SpringerLink
Log in

A hybrid instance-intensive workflow scheduling method in private cloud environment

  • Published:
Natural Computing Aims and scope Submit manuscript

Abstract

Aiming to solve the problem of instance-intensive workflow scheduling in private cloud environment, this paper first formulates a scheduling optimization model considering the communication time between tasks. The objective of this model is to minimize the execution time of all workflow instances. Then, a hybrid scheduling method based on the batch strategy and an improved genetic algorithm termed fragmentation based genetic algorithm is proposed according to the characters of instance-intensive cloud workflow, where task priority dispatching rules are also taken into account. Simulations are conducted to compare the proposed method with the canonical genetic algorithm and two heuristic algorithms. Our simulation results demonstrate that the proposed method can considerably enhance the search efficiency of the genetic algorithm and is able to considerably outperform the compared algorithms, in particular when the number of workflow instances is high and the computational resource available for optimization is limited.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  • Benoit A, Dobrila A, Nicod J-M, Philippe L (2013) Scheduling linear chain streaming applications on heterogeneous systems with failures. Future Gener Comput Syst 29:1140–1151

    Article  Google Scholar 

  • Byun EK, Kee YS, Kim JS, Maeng S (2011) Cost optimized provisioning of elastic resources for application workflows. Future Gener Comput Syst 27:1011–1026

    Article  Google Scholar 

  • Chen W, Da Silva RF, Deelman E, Sakellariou R (2014) Using imbalance metrics to optimize task clustering in scientific workflow executions. Future Gener Comput Syst 46:69–84

    Article  Google Scholar 

  • De Falco I, Scafuri U, Tarantino E (2014) Two new fast heuristics for mapping parallel applications on cloud computing. Future Gener Comput Syst 37:1–13

    Article  Google Scholar 

  • Du W (2009) Toward community-based personal cloud computing. In: Proceedings of world academy of science engineering and technology, p 901

  • Durillo JJ, Nae V, Prodan R (2014) Multi-objective energy-efficient workflow scheduling using list-based heuristics. Future Gener Comput Syst 36:221–236

    Article  Google Scholar 

  • Fard HM, Prodan R, Fahringer T (2014) Multi-objective list scheduling of workflow applications in distributed computing infrastructures. J Parallel Distrib Comput 74:2152–2165

    Article  Google Scholar 

  • Fu Z, Sun X, Liu Q, Zhou L, Shu J (2015) Achieving efficient cloud search services: multi-keyword ranked search over encrypted cloud data supporting parallel computing. IEICE Trans Commun 98(1):190–200

    Article  Google Scholar 

  • Ghorbannia Delavar A, Aryan Y (2014) HSGA: a hybrid heuristic algorithm for workflow scheduling in cloud systems. Clust Comput 17:129–137

    Article  Google Scholar 

  • Hassan M, Song B, Hossain MS, Alamri A (2014) Efficient resource scheduling for big data processing in cloud platform. In: Internet and distributed computing systems, (Lecture notes in computer science), vol 8729. Springer, pp 51–63

  • Huang KC, Tsai YL, Liu HC (2015) Task ranking and allocation in list-based workflow scheduling on parallel computing platform. J Supercomput 71:217–240

    Article  Google Scholar 

  • Huifang L, Siyuan G, Lu Z (2014) A QoS-based scheduling algorithm for instance-intensive workflows in cloud environment. In: The 26th Chinese control and decision conference (2014 CCDC), 31 May 2014–2 June 2014, pp 4094–4099

  • Li W (2010) A community cloud oriented workflow system framework and its scheduling strategy. In: Proceedings 2010 IEEE 2nd symposium on web society

  • Lingfang Z, Veeravalli B, Xiaorong L (2012) ScaleStar: budget conscious scheduling precedence-constrained many-task workflow applications in cloud. In: IEEE 26th international conference on advanced information networking and applications (AINA), 26–29 March 2012, pp 534–541

  • Moattar EZ, Rahmani AM, Derakhshi MRF (2007) Job scheduling in multiprocessor architecture using genetic algorithm. In: 4th IEEE conference on innovations in information technology, pp 248–251

  • Mocanu EM, Florea M, Ionut M (2012) Cloud computing task scheduling based on genetic algorithm. In: System IEEE conference, pp 1–6

  • Nasonov D, Butakov N (2014) Hybrid scheduling algorithm in early warning systems. Procedia Comput Sci 29:1677–1687

    Article  Google Scholar 

  • Pandey S (2010) Scheduling and management of data intensive application workflows in grid and cloud computing environments. Ph.D. dissertation, Department of Computer Science and Software Engineering, University of Melbourne, Melbourne

  • Pereira WF, Bittencourt LF, Da Fonseca NLS (2013) Scheduler for data-intensive workflows in public clouds. In: 2nd IEEE latin American conference on cloud computing and communications (LatinCloud), 9–10 Dec 2013, pp 41–46

  • Ren Y, Shen J, Wang J, Han J, Lee S (2015) Mutual verifiable provable data auditing in public cloud storage. J Internet Technol 16:317–323

    Google Scholar 

  • Rodriguez MA, Buyya R (2014) Deadline based resource provisioningand scheduling algorithm for scientific workflows on clouds. IEEE Trans Cloud Comput 2:222–235

    Article  Google Scholar 

  • Safi Esfahani F, Azmi Murad MA, Sulaiman MNB, Udzir NI (2011) Adaptable decentralized service oriented architecture. J Syst Softw 84:1591–1617

    Article  Google Scholar 

  • Singh L, Singh S (2014) A genetic algorithm for scheduling workflow applications in unreliable cloud environment. In: Martínez Pérez G, Thampi S, Ko R, Shu L (eds) Recent trends in computer networks and distributed systems security, vol 420., Communications in computer and information scienceSpringer, Berlin, pp 139–150

    Chapter  Google Scholar 

  • Vijindra, Shenai S (2012) Survey on scheduling issues in cloud computing. Procedia Eng 38:2881–2888

    Article  Google Scholar 

  • Yu J, Buyya R, Ramamohanarao K (2008) Workflow scheduling algorithms for grid computing. In: Xhafa F, Abraham A (eds) Metaheuristics for scheduling in distributed computing environments, vol 146., Studies in computational intelligenceSpringer, Berlin, pp 173–214

    Chapter  Google Scholar 

  • Yu L, Zhao S, Zhang Y et al (2013) Application of cloud workflow technologies in business intelligence SaaS platform. Comput Intergr Manuf Syst 19:1738–1747

    Google Scholar 

  • Yun-Chia L, Chen AHL, Yung-Hsiang N (2014) Artificial bee colony for workflow scheduling. In: 2014 IEEE congress on evolutionary computation (CEC), 6–11 July 2014, pp 558–564

  • Zeng L, Veeravalli B, Li X (2015) SABA: a security-aware and budget-aware workflow scheduling strategy in clouds. J Parallel Distrib Comput 75:141–151

    Article  Google Scholar 

  • Zhang F, Cao J, Li K, Khan SU, Hwang K (2014) Multi-objective scheduling of many tasks in cloud platforms. Future Gener Comput Syst 37:309–320

    Article  Google Scholar 

  • Zhuo T, Zhenzhen C, Kenli L, Keqin L (2014) An efficient energy scheduling algorithm for workflow tasks in hybrids and DVFS-enabled cloud environment. In: Sixth international symposium on parallel architectures, algorithms and programming (PAAP), 13–15 July 2014, pp 255–261

Download references

Acknowledgements

This research is partially supported by the National Natural Science Foundation of China (Grant No. 71001013), the Fundamental Research Funds for the Central Universities of China (Grant No. DUT13JS11), and the Joint Research Fund for Overseas Chinese, Hong Kong and Macao Scholars of the National Natural Science Foundation of China (Grant No. 61428302).

Author information

Authors and Affiliations

  1. Institute of Information and Decision Technology, Dalian University of Technology, Dalian, Liaoning Province, People’s Republic of China

    Xin Ye, Jia Li, Sihao Liu, Jiwei Liang & Yaochu Jin

  2. Department of Computer Science, University of Surrey, Guildford, GU2 7XH, UK

    Yaochu Jin

Authors
  1. Xin Ye
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jia Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sihao Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jiwei Liang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yaochu Jin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xin Ye.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ye, X., Li, J., Liu, S. et al. A hybrid instance-intensive workflow scheduling method in private cloud environment. Nat Comput 18, 735–746 (2019). https://doi.org/10.1007/s11047-016-9600-3

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11047-016-9600-3

Keywords

Search

Navigation