Skip to main content
SpringerLink
Log in

Eeny Meeny Miny Moe: Choosing the Fault Tolerance Technique for my Cloud Workflow

  • Conference paper
  • First Online:
High Performance Computing (CARLA 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 796))

Included in the following conference series:

Abstract

Scientific workflows are models composed of activities, data and dependencies whose objective is to represent a computer simulation. Workflows are managed by Scientific Workflow Management System (SWfMS). Such workflows commonly demand for many computational resources once their executions may involve a number of different programs processing a huge volume of data. Thus, the use of High Performance Computing (HPC) environments allied to parallelization techniques provides the support for the execution of such experiments. Some resources provided by clouds can be used to build HPC environments. Although clouds offer advantages such as elasticity and availability, failures are a reality rather than a possibility. Thus, SWfMS must be fault-tolerant. There are several types of fault tolerance techniques used in SWfMS such as checkpoint-restart and replication, but which fault tolerance technique best fits with a specific workflow? This work aims at analyzing several fault tolerance techniques in SWfMSs and recommending the suitable one for the user’s workflow using machine learning techniques and provenance data, thus improving resiliency.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    http://docs.aws.amazon.com/cli/latest/reference/ec2/describe-instance-status.html.

  2. 2.

    http://scicumulusc2.wordpress.com/.

  3. 3.

    http://criu.org/.

  4. 4.

    crd.lbl.gov.

  5. 5.

    https://orange.biolab.si/.

References

  1. Mattoso, M., Werner, C., Travassos, G.H., Braganholo, V., Ogasawara, E., de Oliveira, D., et al.: Towards supporting the life cycle of large scale scientific experiments. IJBPIM 5(1), 79+ (2010)

    Article  Google Scholar 

  2. Hoffa, C., Mehta, G., Freeman, T., Deelman, E., Keahey, K., Berriman, B., Good, J.: On the use of cloud computing for scientific workflows. In: eScience 2008, pp. 640–645 (2008)

    Google Scholar 

  3. Vaquero, L.M., Rodero-Merino, L., Caceres, J., Lindner, M.: A break in the clouds: towards a cloud definition. SIGCOMM Rev. 39(1), 50–55 (2008)

    Article  Google Scholar 

  4. Deelman, E., Vahi, K., Juve, G., Rynge, M., Callaghan, S., Maechling, P.J., Mayani, R., Chen, W., da Silva, R.F., Livny, M., et al.: Pegasus, a workflow management system for science automation. FGCS 46, 17–35 (2015)

    Article  Google Scholar 

  5. de Oliveira, D., Ogasawara, E., Baião, F., Mattoso, M.: Scicumulus: a lightweight cloud middleware to explore many task computing paradigm in scientific workflows. In: 3rd International Conference on Cloud Computing, pp. 378–385 (2010)

    Google Scholar 

  6. Jackson, K.R., Ramakrishnan, L., Runge, K.J., Thomas, R.C.: Seeking supernovae in the clouds: a performance study. In: HPDC 2010, pp. 421–429. ACM, New York (2010)

    Google Scholar 

  7. Lee, K.-H., Lai, I.-C., Lee, C.-R.: Optimizing back-and-forth live migration. In: Proceedings of the 9th UCC, UCC 2016, pp. 49–54. ACM, New York (2016). https://doi.org/10.1145/2996890.2996909

  8. Freire, J., Koop, D., Santos, E., Silva, C.T.: Provenance for computational tasks: a survey. Comput. Sci. Eng. 10(3), 11–21 (2008)

    Article  Google Scholar 

  9. Hu, M., Luo, J., Wang, Y., Veeravalli, B.: Adaptive scheduling of task graphs with dynamic resilience. IEEE Trans. Comput. 66(1), 17–23 (2017)

    Article  MathSciNet  MATH  Google Scholar 

  10. Gu, Y., Wu, C.Q., Liu, X., Yu, D.: Distributed throughput optimization for large-scale scientific workflows under fault-tolerance constraint. J. Grid Comput. 11(3), 361–379 (2013)

    Article  Google Scholar 

  11. Bala, A., Chana, I.: Autonomic fault tolerant scheduling approach for scientific workflows in cloud computing. Concurr. Eng. 23(1), 27–39 (2015)

    Article  Google Scholar 

  12. Jain, A., Ong, S.P., Chen, W., Medasani, B., Qu, X., Kocher, M., Brafman, M., Petretto, G., Rignanese, G.-M., Hautier, G., et al.: Fireworks: a dynamic workflow system designed for high-throughput applications. Concurr. Comput. 27(17), 5037–5059 (2015)

    Article  Google Scholar 

  13. Elmroth, E., Hernández, F., Tordsson, J.: A light-weight grid workflow execution engine enabling client and middleware independence. In: Wyrzykowski, R., Dongarra, J., Karczewski, K., Wasniewski, J. (eds.) PPAM 2007. LNCS, vol. 4967, pp. 754–761. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-68111-3_79

    Chapter  Google Scholar 

  14. von Laszewski, G., Hategan, M.: Java cog kit karajan/gridant workflow guide. Technical report, Argonne National Laboratory, Argonne, IL, USA (2005)

    Google Scholar 

  15. Costa, F., de Oliveira, D., Ocaña, K.A.C.S., Ogasawara, E., Mattoso, M.: Enabling re-executions of parallel scientific workflows using runtime provenance data. In: Groth, P., Frew, J. (eds.) IPAW 2012. LNCS, vol. 7525, pp. 229–232. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-34222-6_22

    Chapter  Google Scholar 

  16. Quinlan, J.R.: Induction of decision trees. Mach. Learn. 1(1), 81–106 (1986)

    Google Scholar 

  17. Clark, P., Niblett, T.: The CN2 induction algorithm. Mach. Learn. 3(4), 261–283 (1989)

    Google Scholar 

  18. Zhang, Y., Mandal, A., Koelbel, C., Cooper, K.: Combined fault tolerance and scheduling techniques for workflow applications on computational grids. In: CC-Grid 2009, pp. 244–251. IEEE Computer Society (2009)

    Google Scholar 

  19. Hoheisel, A.: Grid workflow execution service-dynamic and interactive execution and visualization of distributed workflows. In: Proceedings of the Cracow Grid Workshop, vol. 2, pp. 13–24. Citeseer (2006)

    Google Scholar 

  20. Gärtner, F.C.: Fundamentals of fault-tolerant distributed computing in asynchronous environments. ACM CSUR 31(1), 1–26 (1999)

    Article  Google Scholar 

  21. Ocaña, K.A.C.S., de Oliveira, D., Ogasawara, E., Dávila, A.M.R., Lima, A.A.B., Mattoso, M.: SciPhy: a cloud-based workflow for phylogenetic analysis of drug targets in protozoan genomes. In: Norberto de Souza, O., Telles, G.P., Palakal, M. (eds.) BSB 2011. LNCS, vol. 6832, pp. 66–70. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-22825-4_9

    Chapter  Google Scholar 

  22. Saavedra-Barrera, R., Culler, D., Von Eicken, T.: Analysis of multithreaded architectures for parallel computing. In: SPAAACM 1990, pp. 169–178. ACM (1990)

    Google Scholar 

  23. Quinlan, J.R.: Simplifying decision trees. Int. J. Man-Mach. Stud. 27(3), 221–234 (1987)

    Article  Google Scholar 

  24. Ogasawara, E., Dias, J., Silva, V., Chirigati, F., de Oliveira, D., Porto, F., Valduriez, P., Mattoso, M.: Chiron: a parallel engine for algebraic scientific workflows. Concurr. Comput. 25(16), 2327–2341 (2013)

    Article  Google Scholar 

  25. Di, S., Robert, Y., Vivien, F., Kondo, D., Wang, C.-L., Cappello, F.: Optimization of cloud task processing with checkpoint-restart mechanism. In: 2013 International Conference for High Performance Computing, Networking, Storage and Analysis (SC), pp. 1–12. IEEE (2013)

    Google Scholar 

  26. Young, J.W.: A first order approximation to the optimum checkpoint interval. Commun. ACM 17(9), 530–531 (1974)

    Article  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Instituto de Computação, Universidade Federal Fluminense (UFF), Niterói, Brazil

    Leonardo Araújo de Jesus, Lúcia M. A. Drummond & Daniel de Oliveira

Authors
  1. Leonardo Araújo de Jesus
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lúcia M. A. Drummond
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Daniel de Oliveira
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Daniel de Oliveira .

Editor information

Editors and Affiliations

  1. CSC-CONICET and Universidad de Buenos Aires, Buenos Aires, Argentina

    Esteban Mocskos

  2. Universidad de la República, Montevideo, Uruguay

    Sergio Nesmachnow

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

de Jesus, L.A., Drummond, L.M.A., de Oliveira, D. (2018). Eeny Meeny Miny Moe: Choosing the Fault Tolerance Technique for my Cloud Workflow. In: Mocskos, E., Nesmachnow, S. (eds) High Performance Computing. CARLA 2017. Communications in Computer and Information Science, vol 796. Springer, Cham. https://doi.org/10.1007/978-3-319-73353-1_23

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-73353-1_23

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-73352-4

  • Online ISBN: 978-3-319-73353-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation