Advertisement

Programming and Computer Software

, Volume 45, Issue 8, pp 506–516 | Cite as

Data-Oriented Scheduling with Dynamic-Clustering Fault-Tolerant Technique for Scientific Workflows in Clouds

  • Z. AhmadEmail author
  • A. I. JehangiriEmail author
  • M. IftikharEmail author
  • A. I. UmerEmail author
  • I. AfzalEmail author
Article
  • 17 Downloads

Abstract

Cloud computing is one of the most prominent parallel and distributed computing paradigm. It is used for providing solution to a huge number of scientific and business applications. Large scale scientific applications which are structured as scientific workflows are evaluated through cloud computing. Scientific workflows are data-intensive applications, as a single scientific workflow may consist of hundred thousands of tasks. Task failures, deadline constraints, budget constraints and improper management of tasks can also instigate inconvenience. Therefore, provision of fault-tolerant techniques with data-oriented scheduling is an important approach for execution of scientific workflows in Cloud computing. Accordingly, we have presented enhanced data-oriented scheduling with Dynamic-clustering fault-tolerant technique (EDS-DC) for execution of scientific workflows in cloud computing. We have presented data-oriented scheduling as a proposed scheduling technique. We have also equipped EDS-DC with Dynamic-clustering fault-tolerant technique. To know the effectiveness of EDS-DC, we compared its results with three well-known enhanced heuristic scheduling policies referred to as: (a) MCT-DC, (b) Max-min-DC, and (c) Min-min-DC. We considered scientific workflow of CyberShake as a case study, because it contains most of the characteristics of scientific workflows such as integration, disintegration, parallelism, and pipelining. The results show that EDS-DC reduced make-span of 10.9% as compared to MCT-DC, 13.7% as compared to Max-min-DC, and 6.4% as compared to Min-min-DC scheduling policies. Similarly, EDS-DC reduced the cost of 4% as compared to MCT-DC, 5.6% as compared to Max-min-DC, and 1.5% as compared to Min-min-DC scheduling policies. These results in respect of make-span and cost are highly significant for EDS-DC as compared with above referred three scheduling policies. The SLA is not violated for EDS-DC in respect of time and cost constraints, while it is violated number of times for MCT-DC, Max-min-DC, and Min-min-DC scheduling techniques.

REFERENCES

  1. 1.
    Shi, J., Luo, J., Dong, F., Zhang, J., and Zhang, J., Elastic resource provisioning for scientific workflow scheduling in cloud under budget and deadline constraints, Cluster Comput., 2016, vol. 19, no. 1, pp. 167–182.CrossRefGoogle Scholar
  2. 2.
    Sun, D., Chang, G., Miao, C., and Wang, X., “Analyzing, modeling and evaluating dynamic adaptive fault tolerance strategies in cloud computing environments, J. Supercomput., 2013, vol. 66, no. 1, pp. 193–228.CrossRefGoogle Scholar
  3. 3.
    Lifka, D., et al., XSEDE Cloud Survey Report, Urbana, IL: Natl. Center Supercomput. Appl., 2013.Google Scholar
  4. 4.
    Li, X., Song, J., and Huang, B., A scientific workflow management system architecture and its scheduling based on cloud service platform for manufacturing big data analytics, Int. J. Adv. Manuf. Technol., 2016, vol. 84, nos. 1–4, pp. 119–131.CrossRefGoogle Scholar
  5. 5.
    Abbott, B.P., et al., LIGO: the Laser Interferometer Gravitational-Wave Observatory, Rep. Prog. Phys., 2009 vol. 72, no. 7, p. 76901.CrossRefGoogle Scholar
  6. 6.
    Bharathi, S., Deelman, E., Mehta, G., Vahi, K., Chervenak, A., and Su, M., Characterization of scientific workflows, Proc. 3rd Workshop on Workflows in Support of Large Scale Science, Austin, TX, 2008.Google Scholar
  7. 7.
    Callaghan, S., et al., Metrics for heterogeneous scientific workflows : a case study of an earthquake science application, Int. J. High Perform. Comput. Appl., 2011, vol. 25, no. 3, pp. 274-285.CrossRefGoogle Scholar
  8. 8.
    Callaghan, S., et al., Reducing time-to-solution using distributed high-throughput mega-workflows—experiences from SCEC CyberShake, Proc. 4th Int. Conf. on e-Science, e-Science, December 7–12, 2008, Indianapolis, 2008, pp. 151–158.Google Scholar
  9. 9.
    Abrishami, S., Naghibzadeh, M., and Epema, D.H.J., Deadline-constrained workflow scheduling algorithms for Infrastructure as a Service Clouds, Future Gener. Comput. Syst., 2013, vol. 29, no. 1, pp. 158–169.CrossRefGoogle Scholar
  10. 10.
    Chakraborty, D., Mankar, V.V., and Nanavati, A.A., Enabling runtime adaptation of workflows to external events in enterprise environments, Proc. IEEE Int. Conf. on Web Service ICWS 2007, Salt Lake City, 2007, pp. 1112–1119.Google Scholar
  11. 11.
    Deelman, E., Singh, G., Livny, M., Berriman, B., and Good, J., “The cost of doing science on the cloud: the montage example, Proc. Int. Conf. High Performance Computing Networking, Storage and Analysis SC 2008, Austin, TX, 2008.Google Scholar
  12. 12.
    Serrano, D., et al., “SLA guarantees for cloud services, Future Gener. Comput. Syst., 2016, vol. 54, pp. 233–246.CrossRefGoogle Scholar
  13. 13.
    Choi, J., Adufu, T., and Kim, Y., Data-locality aware scientific workflow scheduling methods in HPC cloud environments, Int. J. Parallel Program., 2017, vol. 45, no. 5, pp. 1128–1141.CrossRefGoogle Scholar
  14. 14.
    Tang, W., et al., Data-aware resource scheduling for multicloud workflows: a fine-grained simulation approach, Proc. Int. Conf. on Cloud Computer Technologies and Science CloudCom, Marrakesh, 2015, pp. 887–892.Google Scholar
  15. 15.
    Chen, W. and Deelman, E., “Fault tolerant clustering in scientific workflows, Proc. 8th IEEE World Congr. on Service, Honolulu, 2012, pp. 9–16.Google Scholar
  16. 16.
    Harshitha, S.B., Kaneria, P., and Manjaiah, D.H., Comparative study of workflow scheduling algorithms in cloud computing, Int. J. Innovation Res. Comput. Commun. Eng., 2014, special issue 2, pp. 31–37.Google Scholar
  17. 17.
    Mathew, T., et al., Study and analysis of various task scheduling algorithms in the cloud computing environment, Proc. Int. Conf. on Advances in Computing, Communications and Informatics (ICACCI), September 24–27, 2014, New Delhi, 2014, pp. 658–664.Google Scholar
  18. 18.
    Chen, W. and Deelman, E., WorkflowSim: A toolkit for simulating scientific workflows in distributed environments, Proc. 8th IEEE Int. Conf. E-Science, Chicago, 2012.Google Scholar
  19. 19.
    Kosar, T. and Balman, M., A new paradigm: data-aware scheduling in grid computing, Future Gener. Comput. Syst., 2009, vol. 25, no. 4, pp. 406–413.CrossRefGoogle Scholar
  20. 20.
    Zeng, L., Veeravalli, B., and Zomaya, A.Y., An integrated task computation and data management scheduling strategy for workflow applications in cloud environments, J. Network Comput. Appl., 2015, vol. 50, pp. 39–48.CrossRefGoogle Scholar
  21. 21.
    Poola, D., Ramamohanarao, K., and Buyya, R., Fault-tolerant workflow scheduling using spot instances on clouds, Procedia Comput. Sci., 2014, vol. 29, pp. 523–533.CrossRefGoogle Scholar
  22. 22.
    Kumar, D., Baranwal, G., Raza, Z., and Vidyarthi, D.P., A systematic study of double auction mechanisms in cloud computing, J. Syst. Software, 2017, vol. 125, pp. 234–255.CrossRefGoogle Scholar
  23. 23.
    Malawski, M., Juve, G., Deelman, E., and Nabrzyski, J., Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in IaaS clouds, Future Gener. Comput. Syst., 2015, vol. 48, pp. 1–18.CrossRefGoogle Scholar
  24. 24.
    He, X., Sun, X., and Laszewski, G., A QoS guided scheduling algorithm for grid computing, Office, 2002, vol. 18, no. 4, pp. 1–15.Google Scholar
  25. 25.
    Madureira, A.M. and Definitions, A.B., Ordered minimum completion time heuristic for unrelated parallel-machines problems, Proc. 9th Iberian Conf. on Information Systems and Technologies (CISTI), Barcelona, 2014.Google Scholar
  26. 26.
    Priyadarsini, R.J., Performance evaluation of min-min and max-min algorithms for job scheduling in federated cloud, Int. J. Comput. Appl., 2014, vol. 99, no. 18, pp. 47–54.Google Scholar
  27. 27.
    Qureshi, K., Khan, F.G., Manuel, P., and Nazir, B., A hybrid fault tolerance technique in grid computing system, J. Supercomput., 2011, vol. 56, no. 1, pp. 106–128.CrossRefGoogle Scholar
  28. 28.
    Bala, A. and Chana, I., Fault tolerance-challenges, techniques and implementation in cloud computing, Int. J. Comput. Sci., 2012, vol. 9, no. 1, pp. 288–293.Google Scholar
  29. 29.
    Deelman, E., et al., “Pegasus, a workflow management system for science automation, Future Gener. Comput. Syst., 2015, vol. 46, pp. 17–35.CrossRefGoogle Scholar
  30. 30.
    Tolosana-Calasanz, R., Bañares, J.Á., Pham, C., and Rana, O.F., “Enforcing QoS in scientific workflow systems enacted over Cloud infrastructures, J. Comput. Syst. Sci., 2012, vol. 78, no. 5, pp. 1300–1315.CrossRefGoogle Scholar
  31. 31.
    Chen, W., Ferreira, R., Deelman, E., and Sakellariou, R., Balanced task clustering in scientific workflows, Proc. 9th IEEE Int. Conf. on e-Science, Beijing, 2013, pp. 1–8.Google Scholar
  32. 32.
    Antonescu, A.F. and Braun, T., Simulation of SLA-based VM-scaling algorithms for cloud-distributed applications, Future Gener. Comput. Syst., 2016, vol. 54, pp. 260–273.CrossRefGoogle Scholar
  33. 33.
    Mustafa, S., Nazir, B., Hayat, A., ur Rehman Khan, A., and Madani, S.A., “Resource management in cloud computing: taxonomy, prospects, and challenges, Comput. Electron. Eng., 2015, vol. 47, pp. 186–203.CrossRefGoogle Scholar
  34. 34.
    Barladian, B.Kh., et al., An efficient mulithreading algorithm for the simulation of global illumination, Program. Comput. Software, 2017, vol. 43, no. 4, pp. 217–223.CrossRefGoogle Scholar
  35. 35.
    Fursova, N.I., et al., A lightweight method for virtual machine introspection, Program. Comput. Software, 2017, vol. 43, no. 5, pp. 307–313.CrossRefGoogle Scholar
  36. 36.
    Gusev, A.D., Nasonov, A.V., and Krylov, A.S., Fast parallel grid warping-based image sharpening method, Program. Comput. Software, 2017, vol. 43, no. 4, pp. 230–233.MathSciNetCrossRefGoogle Scholar
  37. 37.
    Kuplyakov, D., Shalnov, E., and Konushin, A., Markov chain Monte Carlo based video tracking algorithm, Program. Comput. Software, 2017, vol. 43, no. 4, pp. 224–229.MathSciNetCrossRefGoogle Scholar
  38. 38.
    Massobrio, R., et al., Towards a cloud computing paradigm for big data analysis in smart cities, Program. Comput. Software, 2018, vol. 44, no. 3, pp. 181–189.CrossRefGoogle Scholar
  39. 39.
    Muruganantham, R. and Ganeshkumar, P., Quality of service enhancement in wireless sensor network using flower pollination algorithm, Program. Comput. Software, 2018, vol. 44, no. 6, pp. 398–406.CrossRefGoogle Scholar
  40. 40.
    Raja, R. and Ganeshkumar, P., QoSTRP: a trusted clustering based routing protocol for mobile ad-hoc networks, Program. Comput. Software, 2018, vol. 44, no. 6, pp. 407–416.CrossRefGoogle Scholar
  41. 41.
    Pashchenko, N.F., Zipa, K.S., and Ignatenko, A.V., An algorithm for the visualization of stereo images simultaneously captured with different exposures, Program. Comput. Software, 2017, vol. 43, no. 4, pp. 250–257.CrossRefGoogle Scholar
  42. 42.
    Varnovskiy, N.P., et al., Secure cloud computing based on threshold homomorphic encryption, Program. Comput. Software, 2015, vol. 41, no. 4, pp. 215–218.MathSciNetCrossRefGoogle Scholar
  43. 43.
    Zelenova, S.A., and Zelenov, S.V., Schedulability analysis for strictly periodic tasks in RTOS, Program. Comput. Software, 2018, vol. 44, no. 3, pp. 159–169.MathSciNetCrossRefGoogle Scholar
  44. 44.
    Zipa, K.S. and Ignatenko, A.V., Algorithms for the analysis and visualization of high dynamic range images based on human perception, Program. Comput. Software, 2016, vol. 42, no. 6, pp. 367–374.MathSciNetCrossRefGoogle Scholar

Copyright information

© Pleiades Publishing, Ltd. 2019

Authors and Affiliations

  1. 1.Department of Information Technology, Hazara UniversityDhodialMansehraPakistan

Personalised recommendations