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

  • Sidra Razzaq
  • Abdul WahidEmail author
  • Faheem Khan
  • Noor ul Amin
  • Munam Ali Shah
  • Adnan Akhunzada
  • Ihsan Ali
Part of the EAI/Springer Innovations in Communication and Computing book series (EAISICC)


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.


Cloud computing High-performance computing Resource allocator and task scheduling 


  1. 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. 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).CrossRefGoogle Scholar
  3. 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.CrossRefGoogle Scholar
  4. 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. 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.CrossRefGoogle Scholar
  6. 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. 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. 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.CrossRefGoogle Scholar
  9. 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. 10.
    Kang, Y., & Zhang, D. (2012). A hybrid genetic scheduling algorithm to heterogeneous distributed system. Applied Mathematics, 3(7), 750.CrossRefGoogle Scholar
  11. 11.
    Shenai, S. (2012). Survey on scheduling issues in cloud computing. Procedia Engineering, 38, 2881–2888.CrossRefGoogle Scholar
  12. 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.CrossRefGoogle Scholar
  13. 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).CrossRefGoogle Scholar
  14. 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.CrossRefGoogle Scholar
  15. 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.CrossRefGoogle Scholar
  16. 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. 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.CrossRefGoogle Scholar
  18. 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.CrossRefGoogle Scholar
  19. 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. 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.CrossRefGoogle Scholar
  21. 21.
    Hassani, R., Aiatullah, M., & Luksch, P. (2014). Improving HPC application performance in public cloud. IERI Procedia, 10, 169–176.CrossRefGoogle Scholar
  22. 22.
    Trinitis, C., & Weidendorfer, J. (2017). Co-scheduling of HPC applications. Amsterdam: IOS Press.Google Scholar
  23. 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. 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.CrossRefGoogle Scholar
  25. 25.
    Intersect360 publishes new five-year HPC market forecast | TOP500 supercomputer sites. [Online]. Retrieved April 25, 2017, from:
  26. 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.CrossRefGoogle Scholar
  27. 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.CrossRefGoogle Scholar
  28. 28.
    Grudenić, I. (2008). Scheduling algorithms and support tools for parallel systems.Google Scholar
  29. 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. 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).CrossRefGoogle Scholar
  31. 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).CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Sidra Razzaq
    • 1
  • Abdul Wahid
    • 1
    Email author
  • Faheem Khan
    • 2
  • Noor ul Amin
    • 2
  • Munam Ali Shah
    • 1
  • Adnan Akhunzada
    • 1
  • Ihsan Ali
    • 3
  1. 1.Department of Computer ScienceCOMSATS Institute of Information TechnologyIslamabadPakistan
  2. 2.Department of Computer ScienceBacha Khan UniversityCharsaddaPakistan
  3. 3.Department of Computer Systems and Technology, Faculty of Computer Science and Information TechnologyUniversity of MalayaKuala LumpurMalaysia

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