Skip to main content
SpringerLink
Log in

Scheduling Algorithms for High-Performance Computing: An Application Perspective of Fog Computing

  • Chapter
  • First Online:
Recent Trends and Advances in Wireless and IoT-enabled Networks

Part of the book series: EAI/Springer Innovations in Communication and Computing ((EAISICC))

Abstract

High-performance computing (HPC) demands many computers to perform multiple tasks concurrently and efficiently. For efficient resource utilization and for better response time, different scheduling algorithms have been proposed which aim to increase throughput, scalability, and performance of HPC applications. In this paper, our contribution is twofold. Firstly, the classification of scheduling algorithms on the basis of multiple factors like throughput, waiting time, fairness, overhead, etc. is presented. This paper investigates the recent research that has been carried out from 2009–2017. With this categorization, we aim to provide an easy and concise view of the HPC algorithms. Secondly, the forecasting has been done on HPC applications to predict the growth rate for 2020 and beyond.

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 99.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 129.99
Price excludes VAT (USA)
  • Durable hardcover 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. Gupta, A., & Milojicic, D. (2011). Evaluation of HPC applications on cloud. In OCS’11 Proceedings of the 2011 Sixth, Open Cirrus Summit (OCS).

    Google Scholar 

  2. Gupta, A., Kale, L. V., Gioachin, F., March, V., Suen, C. H., Lee, B.-S., et al. (2013). The who, what, why, and how of high performance computing in the cloud. In 2013 IEEE 5th International Conference on Cloud Computing Technology and Science (pp. 306–314).

    Chapter  Google Scholar 

  3. Balis, B., Figiela, K., Jopek, K., Malawski, M., & Pawlik, M. (2017). Porting HPC applications to the cloud: A multi-frontal solver case study. Journal of Computational Science, 18, 106–116.

    Article  Google Scholar 

  4. Shimpy, E., & Sidhu, J. (2014). Different scheduling algorithms in different cloud environment. International Journal of Advanced Research in Computer and Communication Engineering, 3(9), 2278–1021.

    Google Scholar 

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

    Article  Google Scholar 

  6. Georgiou, Y., Jeannot, E., Mercier, G., & Villiermet, A. (2017). Topology-aware resource management for HPC applications. In ICDCN ’17 Proceedings of the 18th International Conference on Distributed Computing and Networking (pp. 1–10).

    Google Scholar 

  7. Roloff, E., Diener, M., & Carissimi, A. (2012). High performance computing in the cloud: Deployment, performance and cost efficiency. In 2012 IEEE 4th International Conference on Cloud Computing Technology and Science (CloudCom). Piscataway, NJ: IEEE.

    Google Scholar 

  8. Gupta, A., Faraboschi, P., Gioachin, F., Kale, L. V., Kaufmann, R., Lee, B.-S., et al. (2016). Evaluating and improving the performance and scheduling of HPC applications in cloud. IEEE Transaction on Cloud Computing, 4(3), 307–321.

    Article  Google Scholar 

  9. Jang, S. H., Kim, T. Y., & Kim, J. K. (2012). The study of genetic algorithm-based task scheduling for cloud computing. International Journal of Control and Automation, 5(4), 157–162.

    Google Scholar 

  10. Kang, Y., & Zhang, D. (2012). A hybrid genetic scheduling algorithm to heterogeneous distributed system. Applied Mathematics, 3(7), 750.

    Article  Google Scholar 

  11. Shenai, S. (2012). Survey on scheduling issues in cloud computing. Procedia Engineering, 38, 2881–2888.

    Article  Google Scholar 

  12. Zhan, Z.-H., Liu, X.-F., Gong, Y.-J., Zhang, J., Chung, H. S.-H., & Li, Y. (2015). Cloud computing resource scheduling and a survey of its evolutionary approaches. ACM Computing Survey, 47(4), 1–33.

    Article  Google Scholar 

  13. Dillon, T., Wu, C., & Chang, E. (2010). Cloud computing: Issues and challenges. In 2010 24th IEEE International Conference on Advanced Information Networking and Applications (pp. 27–33).

    Chapter  Google Scholar 

  14. Alkhashai, H. M., & Omara, F. A. (2016). An enhanced task scheduling algorithm on cloud computing environment. International Journal of Grid and Distributed Computing, 9(7), 91–100.

    Article  Google Scholar 

  15. Abdelaziz, A., Fong, A. T., Gani, A., Garba, U., Khan, S., Akhunzada, A., et al. (2017). Distributed controller clustering in software defined networks. PLoS One, 12(4), e0174715.

    Article  Google Scholar 

  16. Akhunzada, A., Gani, A., Hussain, S., & Khan, A. A. (2015). A formal framework for web service broker to compose QoS measures. In 2015 SAI Intelligent Systems Conference (IntelliSys). Piscataway, NJ: IEEE.

    Google Scholar 

  17. Tsai, J.-T., Fang, J.-C., & Chou, J.-H. (2013). Optimized task scheduling and resource allocation on cloud computing environment using improved differential evolution algorithm. Computers and Operation Research, 40(12), 3045–3055.

    Article  Google Scholar 

  18. Iosup, A., Ostermann, S., & Yigitbasi, M. (2011). Performance analysis of cloud computing services for many-tasks scientific computing. IEEE Transactions on Parallel and Distributed Systems, 22(6), 931–945.

    Article  Google Scholar 

  19. Garg, S., Yeo, C., Anandasivam, A., & Buyya, R. (2009). Energy-efficient scheduling of HPC applications in cloud computing environments. arXiv Prepr. arXiv.

    Google Scholar 

  20. Bahnasawy, N. A., Omara, F., Koutb, M. A., & Mosa, M. (2011). Optimization procedure for algorithms of task scheduling in high performance heterogeneous distributed computing systems. Egyptian Informatics Journal, 12(3), 219–229.

    Article  Google Scholar 

  21. Hassani, R., Aiatullah, M., & Luksch, P. (2014). Improving HPC application performance in public cloud. IERI Procedia, 10, 169–176.

    Article  Google Scholar 

  22. Trinitis, C., & Weidendorfer, J. (2017). Co-scheduling of HPC applications. Amsterdam: IOS Press.

    Google Scholar 

  23. Desai, N., & Cirne, W. (2014). Job Scheduling Strategies for Parallel Processing: 17th International Workshop, JSSPP 2013, Boston, MA, USA, May 24, 2013 Revised Selected Papers (Vol. 8429). Berlin: Springer.

    Google Scholar 

  24. Yang, C., Huang, Q., Li, Z., Liu, K., & Hu, F. (2017). Big data and cloud computing: Innovation opportunities and challenges. International Journal of Digital Earth, 10(1), 13–53.

    Article  Google Scholar 

  25. Intersect360 publishes new five-year HPC market forecast | TOP500 supercomputer sites. [Online]. Retrieved April 25, 2017, from: https://www.top500.org/news/intersect360-publishes-new-five-year-hpc-market-forecast/

  26. Cui, H., Liu, X., Yu, T., Zhang, H., Fang, Y., & Xia, Z. (2017). Cloud service scheduling algorithm research and optimization. Security and Communication Networks, 2017, 7.

    Article  Google Scholar 

  27. Rodriguez, M. A., & Buyya, R. (2016). A taxonomy and survey on scheduling algorithms for scientific workflows in iaas cloud computing environments. Concurrency and Computation: Practice and Experience, 29(8), e4041.

    Article  Google Scholar 

  28. Grudenić, I. (2008). Scheduling algorithms and support tools for parallel systems.

    Google Scholar 

  29. Xoxa, N., Zotaj, M., Tafa, I., & Fejzaj, J. (2014). Simulation of first come first served (FCFS) and shortest job first (SJF) algorithms. International Journal of Computer science and Network, 3(6), 444–449.

    Google Scholar 

  30. Mittal, S., & Katal, A. (2016). An optimized task scheduling algorithm in cloud computing. In 2016 IEEE 6th International Conference on Advanced Computing (IACC), vol. 7, no. 4 (pp. 197–202).

    Chapter  Google Scholar 

  31. Nosheen, F., & Bibi, S. (2013). Ant Colony optimization based scheduling algorithm. In 2013 International Conference on Open Source Systems and Technologies (ICOSST) (pp. 18–22).

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, COMSATS Institute of Information Technology, Islamabad, Pakistan

    Sidra Razzaq, Abdul Wahid, Munam Ali Shah & Adnan Akhunzada

  2. Department of Computer Science, Bacha Khan University, Charsadda, Pakistan

    Faheem Khan & Noor ul Amin

  3. Department of Computer Systems and Technology, Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, Malaysia

    Ihsan Ali

Authors
  1. Sidra Razzaq
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Abdul Wahid
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Faheem Khan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Noor ul Amin
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Munam Ali Shah
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Adnan Akhunzada
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Ihsan Ali
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Abdul Wahid .

Editor information

Editors and Affiliations

  1. Abdul Wali Khan University Mardan, Mardan, Pakistan

    Mian Ahmad Jan

  2. Abdul Wali Khan University Mardan, Mardan, Pakistan

    Fazlullah Khan

  3. Department of Computer Science and Software Engineering, Xi’an Jiaotong-Liverpool University, Suzhou, Jiangsu Province, China

    Muhammad Alam

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Razzaq, S. et al. (2019). Scheduling Algorithms for High-Performance Computing: An Application Perspective of Fog Computing. In: Jan, M., Khan, F., Alam, M. (eds) Recent Trends and Advances in Wireless and IoT-enabled Networks. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-319-99966-1_10

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-99966-1_10

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-99965-4

  • Online ISBN: 978-3-319-99966-1

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation