Skip to main content
Log in

A Conceptual Design of Job Pre-processing Flow for Heterogeneous Batch Systems in Data Center

  • Special Issue "Convergence Interaction for Communication'"
  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

Processing a workflow (or a job) created by a user, who can be a researcher from a scientific laboratory or an analysis from a commercial organization, is the main functionality that a data center or a high-performance computing center is generally expected to provide. It can be accomplished with a single core processor and rather small amount of memory if the problem is adequately small while it may require thousands of nodes to solve a complicated problem and peta-bytes of storage for its output. Also specific applications on various platforms are required in general by users for resolving the problems appropriately. In this aspect, a data center should operate non-homogeneous systems for resource management, so-called batch system, in which it results in inefficient resource utilization due to stochastic behavior of user activity. Implementation of virtualization for resource management, e.g. Cloud Computing, is one of promising solutions recently arising, however, it results in the increase of complexity of the system itself as well as the system administration because it naturally implies the intervention of virtualization stack, e.g. hypervisor, between Operating System and applications for resource management. In this paper, we propose a new conceptual design to be implemented as a pre-scheduler capable to insert user submitted jobs dedicated to a specific batch system into available resources managed by other kind of batch systems. The proposed design features transparency in between clients and batch systems, accuracy in terms of monitoring and prediction on the available resources, and scalability for additional batch systems. We suggest the implementation example of the conceptual design based on the scenario established from our experience of operating a data center.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Bandyopadhyay, D., & Sen, J. (2011). Internet of things: Applications and challenges in technology and standardization. Wireless Personal Communications, 58(1), 49–69.

    Article  Google Scholar 

  2. Park, S.-T., Kim, Y.-R., Jeong, S.-P., Hong, C.-I., & Kang, T.-G. (2016). A case study on effective technique of distributed data storage for big data processing in the wireless internet environment. Wireless Personal Communications, 86(1), 239–253.

  3. Marias, G. F., Prigouris, N., Papazafeiropoulos, G., Hadjiefthymiades, S., & Merakos, L. (2004). Brokering positioning data from heterogeneous infrastructures. Wireless Personal Communications, 30(2–4), 233–245.

    Article  Google Scholar 

  4. Hager, M., Finke, T., Seitz, J., & Waas, T. (2014). Software-based management for ethernet networks. Wireless Personal Communications, 74(3), 1021–1032.

    Article  Google Scholar 

  5. Lamanna, M. (2004). The LHC computing grid project at CERN. Nuclear Instruments & Methods in Physics Research, Section A: Accelerators, Spectrometers, Detectors, and Associated Equipment, 534(1–2), 1–6.

    Article  Google Scholar 

  6. Evans, L., & Bryant, P. (2008). LHC machine. Journal of Instrumentation, 3, S08001.

    Article  Google Scholar 

  7. Gagliardi, F. (2004). The EGEE European grid infrastructure project. In Proceedings of High Performance Computing for Computational Science, pp. 194–203.

  8. Pordes, R., Petravick, D., Kramer, B., Olson, D., Livny, M., Roy, A., et al. (2007). The open science grid. Journal of Physics: Conference Series, 78(1), 012057.

    Google Scholar 

  9. Ahn, S. U., Yeo, I. Y., & Park, S. O. (2014). Secure and efficient high-performance PROOF-based cluster system for high-energy physics. Journal of Supercomputing, 70(1), 166–176.

    Article  Google Scholar 

  10. Tera-scale Open-source Resource and QUEue manager (TORQUE), class. http://www.clusterresources.com/pages/products/torque-resource-manager.php.

  11. Litzkow, M., & Livny, M. (1990). Experience with the condor distributed batch system. In Proceedings of IEEE Workshop on Experimental Distributed Systems, pp. 97–101.

  12. Henderson, R. L. (1995). Job scheduling under the portable batch system. In Proceedings of Workshop on Job Scheduling Strategies for Parallel Processing, pp. 279–294.

  13. OpenPBS. http://www.mcs.anl.gov/research/projects/openpbs/.

  14. Nitzberg, B., Schopf, J. M., & Jones, J. P. (2004). PBS Pro: Grid computing and scheduling attributes. Grid Resource Management, 64, 183–190.

    Article  Google Scholar 

  15. Gentzsch, W. (2001). Sun grid engine: Towards creating a compute power grid. In Proceedings of First IEEE/ACM International Symposium on Cluster Computing and the Grid, pp. 35–36.

  16. Univa Grid Engine. http://www.univa.com/products/grid-engine.php.

  17. Zhou, S. (1992). LSF: Load sharing in large heterogeneous distributed systems. In Proceedings of Workshop on Cluster Computing.

  18. Prenneis Jr., A. (1996). Loadleveler: Workload management for parallel and distributed computing environments. In Proceedings of Supercomputing Europe, pp. 176.

  19. Yoo, A. B., Jette, M. A., & Grondona, M. (2003). Slurm: Simple linux utility for resource management. In Proceedings of Workshop on Job Scheduling Strategies for Parallel Processing, pp. 44–60.

  20. Yan, Y., & Chapman, B. (2008). Comparative study of distributed resource management systems-SGE, LSF, PBS Pro, and LoadLeveler”, Technical Report-Citeseerx.

  21. Drozdowski, M. (2009). Scheduling for parallel processing. London: Springer.

    Book  MATH  Google Scholar 

  22. Feitelson, D. G., Rudolph, L., & Schwiegelshohn, U. (2005). Parallel job scheduling—A status report. In Proceedings of Job Strategies for Parallel Processing, pp. 1–16.

  23. Foster, I., & Kesselman, C. (2003). The Grid 2: Blueprint for a new computing infrastructure. Philadelphia: Elsevier.

    Google Scholar 

  24. Garzoglio, G., Levshina, T., Mhashilkar, P., & Timm, S. (2009). ReSS: A resource selection service for the open science grid. In S. C. Lin & E. Yen (Eds.), Grid Computing (pp. 89–98). Boston, MA: Springer.

    Chapter  Google Scholar 

  25. Kim, C. W., Yoon, H., Jin, D., & Park, S. O. (2015). Integrated management system for a large computing resources in a scientific data center. Journal of Supercomputing,. doi:10.1007/s11227-015-1480-2.

    Google Scholar 

  26. Yoon, H., Yeo, I. Y., & Kim, J. H. (2014). Updating the trusted connection of re-organized computing resource under the automated system management platform. Journal of Supercomputing, 70(1), 200–210.

    Article  Google Scholar 

  27. Zhang, Q., Cheng, L., & Boutaba, R. (2010). Cloud computing: state-of-the-art and research challenges. Journal of Internet Services and Applications, 1(1), 7–18.

    Article  Google Scholar 

  28. Jackson, D., Snell, Q., & Clement, M. (2001). Core algorithms of the Maui scheduler. In Proceedings of Workshop on Job Scheduling Strategies for Parallel Processing, pp. 87–102.

Download references

Acknowledgments

This research was supported by the National Research Foundation of Korea (NRF) through the contract N-15-NM-CR01-S01.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jin Kim.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ahn, S.U., Kim, J. A Conceptual Design of Job Pre-processing Flow for Heterogeneous Batch Systems in Data Center. Wireless Pers Commun 89, 847–861 (2016). https://doi.org/10.1007/s11277-016-3224-x

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-016-3224-x

Keywords

Navigation