Skip to main content
SpringerLink
Log in

Orlando Tools: Development, Training, and Use of Scalable Applications in Heterogeneous Distributed Computing Environments

  • Conference paper
  • First Online:
Book cover High Performance Computing (CARLA 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 979))

Included in the following conference series:

Abstract

We address concepts and principles of the development, training, and use of applications in heterogeneous environments that integrate different computational infrastructures including HPC-clusters, grids, and clouds. Existing differences in the Grid and cloud computing models significantly complicate problem-solving processes in such environments for end-users. In this regards, we propose the toolkit named Orlando Tools for creating scalable applications for solving large-scale scientific and applied problems. It provides mechanisms for the subject domain specification, problem formulation, problem-solving time prediction, problem-solving scheme execution, monitoring, etc. The toolkit supports hands-on training skills for end-users. To demonstrate the practicability and benefits of Orlando Tools, we present an example of the development and use of the scalable application for solving practical problems of warehouse logistics.

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

References

  1. Zhao, Y., Fei, Z., Raicu, I., Lu, S.: Opportunities and challenges in running scientific workflows on the cloud. In: Kumar, A., Xie, B., Lu, D. (eds.) Proceedings of the International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, pp. 455–462. IEEE, Piscataway (2011)

    Google Scholar 

  2. Zhan, Z.H., Liu, X.F., Gong, Y.J., Zhang, J., Chung, H.S.H., Li, Y.: Cloud computing resource scheduling and a survey of its evolutionary approaches. ACM Comput. Surv. 47(4), 1–33 (2015)

    Article  Google Scholar 

  3. Sokolinsky, L.B., Shamakina, A.V.: Methods of resource management in problem-oriented computing environment. Program. Comput. Softw. 42(1), 17–26 (2016)

    Article  MathSciNet  Google Scholar 

  4. Hollinsworth, D.: The workflow reference model. In: Zur Muehlen, M., Allen, R. (eds.) Workflow Management Coalition, Document no. TC00-1003 (1995)

    Google Scholar 

  5. Sowa, J.: Conceptual Structures – Information Processing in Mind and Machine. Addison-Wesley, Boston (1984)

    MATH  Google Scholar 

  6. Tyugu, E.: Knowledge-Based Programming. Turing Institute Press, Glasgow (1988)

    MATH  Google Scholar 

  7. Oracle Grid Engine. http://www.oracle.com/technetwork/oem/grid-engine-166852.html. Accessed 31 Jan 2018

  8. Torque Resource Manager. http://www.adaptivecomputing.com/products/open-source/torque. Accessed 31 Jan 2018

  9. HTCondor. http://research.cs.wisc.edu/htcondor. Accessed 31 Jan 2018

  10. Slurm Workload Manager. http://slurm.net. Accessed 31 Jan 2018

  11. GridWay Metascheduler. http://www.gridway. Accessed 31 Jan 2018

  12. Frey, J., Tannenbaum, T., Livny, M.: Condor-G: a computation management agent for multi-institutional grids. Cluster Comput. 5(3), 237–246 (2002)

    Article  Google Scholar 

  13. Tao, J., Kolodziej, J., Ranjan, R., Jayaraman, P., Buyya, R.: A note on new trends in data-aware scheduling and resource provisioning in modern HPC systems. Future Gener. Commun. Syst. 51(C), 45–46 (2015)

    Google Scholar 

  14. Rings, T., et al.: Grid and cloud computing: opportunities for integration with the next generation network. J. Grid Comput. 7(3), Article no. 375 (2009)

    Google Scholar 

  15. Basili, V.R., et al.: Understanding the high-performance-computing community: a software engineer’s perspective. IEEE Softw. 25(4), 29–36 (2008)

    Article  Google Scholar 

  16. Joppa, L.N., et al.: Troubling trends in scientific software use. Science 340(6134), 814–815 (2013)

    Article  Google Scholar 

  17. Nunez, A., Merayo, M.G.: A formal framework to analyze cost and performance in map-reduce based applications. J. Comput. Sci. 5(2), 106–118 (2014)

    Article  Google Scholar 

  18. Pandey, S., Wu, L., Guru, S.M., Buyya, R.: A particle swarm optimization-based heuristic for scheduling workflow applications in cloud computing environments. In: 24th IEEE International Conference on Advanced Information Networking and Applications, pp. 400–407. IEEE (2010)

    Google Scholar 

  19. Yu, J., Buyya, R.: A taxonomy of workflow management systems for grid computing. J. Grid Comput. 3(3–4), 171–200 (2005)

    Article  Google Scholar 

  20. Barker, A., van Hemert, J.: Scientific workflow: a survey and research directions. In: Wyrzykowski, R., Dongarra, J., Karczewski, K., Wasniewski, J. (eds.) PPAM 2007. LNCS, vol. 4967, pp. 746–753. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-68111-3_78

    Chapter  Google Scholar 

  21. Murugan, S., Kumar, S.: A survey of workflow management tools for grid platform. Adv. Inform. Technol. Manage. 1(1), 1–3 (2012)

    Google Scholar 

  22. Smirnov, P.A., Kovalchuk, S.V., Boukhanovsky, A.V.: Knowledge-based support for complex systems exploration in distributed problem solving environments. Commun. Comput. Inf. Sci. 394, 147–161 (2013)

    Google Scholar 

  23. Kliazovich, D., Pecero, J.E., Tchernykh, A., Bouvry, P., Khan, S.U., Zomaya, A.Y.: CA-DAG: modeling communication-aware applications for scheduling in cloud computing. J. Grid Comput. 14(1), 22–39 (2016)

    Article  Google Scholar 

  24. Talia, D.: Workflow systems for science: concepts and tools. ISRN Softw. Eng. 2013, 15 (2013). Article ID 404525

    Article  Google Scholar 

  25. Rodriguez, A., Tchernykh, A., Ecker, K.: Algorithms for dynamic scheduling of unit execution time tasks. Eur. J. Oper. Res. 146(2), 403–416 (2003)

    Article  MathSciNet  Google Scholar 

  26. Nesmachnow, S., Iturriaga, S., Dorronsoro, B., Tchernykh, A.: Multiobjective energy-aware workflow scheduling in distributed datacenters. Commun. Comput. Inf. Sci. 595, 79–93 (2016)

    Google Scholar 

  27. Cristobal, A., Tchernykh, A., Gaudiot, J.-L., Lin, W.-Y.: Non-strict execution in parallel and distributed computing. Int. J. Parallel Prog. 31(2), 77–105 (2003)

    Article  Google Scholar 

  28. Tchernykh, A., Schwiegelsohn, U., Alexandrov, V., Talbi, E.G.: Towards understanding uncertainty in cloud computing resource provisioning. Procedia Comput. Sci. 51, 1772–1781 (2015)

    Article  Google Scholar 

  29. Bychkov, I., Oparin, G., Tchernykh, A., Feoktistov, A., Bogdanova, V., Gorsky, S.: Conceptual model of problem-oriented heterogeneous distributed computing environment with multi-agent management. Procedia Comput. Sci. 103, 162–167 (2017)

    Article  Google Scholar 

  30. Intel® VTune™ Amplifier. https://software.intel.com/en-us/intel-vtune-amplifier-xe. Accessed 20 Apr 2018

  31. Bychkov, I.V., Oparin, G.A., Feoktistov, A.G., Sidorov, I.A., Bogdanova, V.G., Gorsky, S.A.: Multiagent control of computational systems on the basis of meta-monitoring and imitational simulation. Optoelectron. Instrum. Data Process. 52(2), 107–112 (2016)

    Article  Google Scholar 

  32. Giarratano, J.C., Riley, G.D.: Expert Systems: Principles and Programming. Thomson, Boston (2005)

    Google Scholar 

  33. Feoktistov, A.G., Sidorov, I.A.: Logical-probabilistic analysis of distributed computing reliability. 39th International Convention on Information and Communication Technology, Electronics and Microelectronics, pp. 247–252. IEEE, Riejka (2016)

    Google Scholar 

Download references

Acknowledgment

The study was partially supported by RFBR, projects no. 16-07-00931-a and no. 18-07-01224-a. Part of the work was supported by the Program of basic scientific research of the RAS, project no. IV.38.1.1.

Author information

Authors and Affiliations

  1. CICESE Research Center, Ensenada, Mexico

    Andrei Tchernykh & Raul Rivera-Rodriguez

  2. Matrosov Institute for System Dynamics and Control Theory of SB RAS, Irkutsk, Russia

    Alexander Feoktistov, Sergei Gorsky, Ivan Sidorov, Roman Kostromin & Igor Bychkov

  3. Irkutsk State University, Irkutsk, Russia

    Olga Basharina

  4. South Ural State University, Chelyabinsk, Russia

    Andrei Tchernykh

  5. ICREA-BSC, Barcelona, Spain

    Vassil Alexandrov

Authors
  1. Andrei Tchernykh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Alexander Feoktistov
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sergei Gorsky
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ivan Sidorov
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Roman Kostromin
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Igor Bychkov
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Olga Basharina
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Vassil Alexandrov
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Raul Rivera-Rodriguez
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Andrei Tchernykh .

Editor information

Editors and Affiliations

  1. Instituto Tecnológico de Costa Rica, Centro Nacional de Alta Tecnología , Pavas, Costa Rica

    Esteban Meneses

  2. Universidad de los Andes, Bogotá, Colombia

    Harold Castro

  3. Universidad Industrial de Santander, Bucaramanga, Colombia

    Carlos Jaime Barrios Hernández

  4. Universidad de Antioquia, Medellín, Colombia

    Raul Ramos-Pollan

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Tchernykh, A. et al. (2019). Orlando Tools: Development, Training, and Use of Scalable Applications in Heterogeneous Distributed Computing Environments. In: Meneses, E., Castro, H., Barrios Hernández, C., Ramos-Pollan, R. (eds) High Performance Computing. CARLA 2018. Communications in Computer and Information Science, vol 979. Springer, Cham. https://doi.org/10.1007/978-3-030-16205-4_20

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-16205-4_20

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-16204-7

  • Online ISBN: 978-3-030-16205-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation