Skip to main content

Advertisement

Log in

ExaLB: a mathematical framework for load balancing to support distributed exascale computing environments

  • Regular Paper
  • Published:
CCF Transactions on High Performance Computing Aims and scope Submit manuscript

    We’re sorry, something doesn't seem to be working properly.

    Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.

Abstract

The dynamic and interactive nature of Distributed Exascale Computing System leads to a situation where the load balancer lacks the proper pattern for the solution. In addition to analyzing and reviewing the dynamic and interactive nature and its effect on load balancing, this article introduces a framework for managing load balancing that does not need to study the dynamic and interactive nature. This framework proposes a mathematical scheme for the functionality of load-balancing elements and redefines its functions and components. The redefinition makes it possible to determine the constituent parts of the framework and their functionality without the need to analyze the dynamic and interactive nature of the system. The proposed framework can manage and control dynamic and interactive events by reviewing changes in the functionality of resources, the pattern of data collection to execute processes related to the load balancer, and a Scalable tool. In addition to performing the load balancer’s functionality, our framework can continue to function under dynamic and interactive events in distributed exascale systems. On average, this framework has a 43% improvement, unable to respond to dynamic and interactive requests.

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

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  • Adibi, E., Khaneghah, E.M.: ExaRD: introducing a framework for empowerment of resource discovery to support distributed exascale computing systems with high consistency. Clust. Comput. 23, 1–21 (2020)

    Article  Google Scholar 

  • Alowayyed, Saad et al. "Multiscale computing in the exascale era." Journal of Computational Science 22 (2017): 15–25.

  • Amelina, N., Fradkov, A., Jiang, Y., Vergados, D.J.: Approximate consensus in stochastic networks with application to load balancing. IEEE Trans. Inf. Theory 61(4), 1739–1752 (2015)

    Article  MathSciNet  Google Scholar 

  • Bakhishoff, U., et al.: DTHMM ExaLB: discrete-time hidden Markov model for load balancing in distributed exascale computing environment. Cogent Eng. 7(1), 1743404 (2020)

    Article  Google Scholar 

  • Bok, K. et al.: Load Balancing with Load Threshold Adjustment in Structured P2P. In: 2018 IEEE International Conference on Big Data and Smart Computing (BigComp). IEEE (2018)

  • Chatterjee, Moumita, and S. K. Setua. "A new clustered load balancing approach for distributed systems." Computer, Communication, Control and Information Technology (C3IT), 2015 Third International Conference on. IEEE, 2015.

  • Domanal, S.G., Reddy, G.R.M.: Optimal load balancing in cloud computing by efficient utilization of virtual machines. In: Communication Systems and Networks (COMSNETS), 2014 Sixth International Conference on. IEEE, pp. 1–4 (2014)

  • Dongarra, J., et al.: The international exascale software project roadmap. Int. J. High Perform. Comput. Appl. 25(1), 3–60 (2011)

    Article  Google Scholar 

  • Dongarra, J., Hittinger, J., Bell, J., Chacon, L., Falgout, R., Heroux, M. et al.: Applied Mathematics Research for Exascale Computing (No. LLNL-TR-651000). Lawrence Livermore National Lab (LLNL), Livermore, (2014)

  • Fiore, S., Bakhouya, M., Smari, W.W.: On the road to exascale: advances in high performance computing and simulations—an overview and editorial. Future Gen. Comput. Syst. 82, 450–458 (2018)

    Article  Google Scholar 

  • Gharb, H., et al.: Challenges of execution trend in distributed exascale system. JDCS 1(2), 140–151 (2019)

    Google Scholar 

  • Ghomi, E.J., Rahmani, A.M., Qader, N.N.: Load-balancing algorithms in cloud computing: a survey. J. Netw. Comput. Appl. 88, 50–71 (2017)

    Article  Google Scholar 

  • Heidsieck, G. et al.: Adaptive caching for data-intensive scientific workflows in the cloud. In: International Conference on Database and Expert Systems Applications. Springer, Cham (2019)

  • http://www.deep-project.EU. Accessed Oct 2022

  • Innocenti, Maria Elena et al. "Progress towards physics-based space weather forecasting with exascale computing." Advances in Engineering Software 111 (2017): 3–17.

  • Jain, S., Saxena, A.K.: A survey of load balancing challenges in cloud environment. In: 2016 International conference system modeling & advancement in research trends (SMART). IEEE (2016)

  • Jeannot, E., Mercier, G., Tessier, F.: Topology and affinity aware hierarchical and distributed load-balancing in Charm++. In: Communication Optimizations in HPC (COMHPC), International Workshop on, pp. 63–72 (2016)

  • Jiang, Y.: A survey of task allocation and load balancing in distributed systems. IEEE Trans. Parallel Distrib. Syst. 27(2), 585–599 (2016)

    Article  Google Scholar 

  • Jyoti, A., Shrimali, M.: Dynamic provisioning of resources based on load balancing and service broker policy in cloud computing. Clust. Comput. 23(1), 377–395 (2020)

    Article  Google Scholar 

  • Kayal, N.: The complexity of the annihilating polynomial. In: 2009 24th Annual IEEE conference on computational complexity. IEEE (2009)

  • Khan, S., et al.: Load balancing in grid computing: taxonomy, trends and opportunities. J. Netw. Comput. Appl. 88, 99–111 (2017)

    Article  Google Scholar 

  • Khaneghah, E.M.: U.S. Patent No. 9,613,312. Washington, DC: U.S. Patent and Trademark Office (2017)

  • Khaneghah, E.M., Sharifi, M.: AMRC: an algebraic model for reconfiguration of high performance cluster computing systems at runtime. J. Supercomput. 67(1), 1–30 (2014)

    Article  Google Scholar 

  • Khaneghah, E.M., ShowkatAbad, A.R., Ghahroodi, R.N.: Challenges of process migration to support distributed exascale computing environment. In: Proceedings of the 2018 7th International Conference on Software and Computer Applications (2018)

  • Khaneghah, E.M. et al.: Challenges of load balancing to support distributed exascale computing environment. In: Proceedings of the International Conference on Parallel and Distributed Processing Techniques and Applications (PDPTA). The Steering Committee of The World Congress in Computer Science, Computer Engineering and Applied Computing (WorldComp) (2018)

  • Kołodziej, J., Khan, S.U., Wang, L., Kisiel-Dorohinicki, M., Madani, S.A., Niewiadomska-Szynkiewicz, E., et al.: Security, energy, and performance-aware resource allocation mechanisms for computational grids. Future Gen. Comput. Syst. 31, 77–92 (2014)

    Article  Google Scholar 

  • Lehman, C., Nookala, P., Raicu I.: Scalable load-balancing concurrent queues in modern many-core architectures. In: SC19. ACM (2019)

  • Lieber, M., Gößner, K., & Nagel, W. E.: The potential of diffusive load balancing at large scale. In: Proceedings of the 23rd European MPI Users' Group Meeting. ACM, pp. 154–157 (2016)

  • Milani, A.S., Navimipour, N.J.: Load balancing mechanisms and techniques in the cloud environments: systematic literature review and future trends. J. Netw. Comput. Appl. 71, 86–98 (2016)

    Article  Google Scholar 

  • Mirtaheri, S.L., Grandinetti, L.: Dynamic load balancing in distributed exascale computing systems. Clust. Comput. 20(4), 3677–3689 (2017)

    Article  Google Scholar 

  • Mirtaheri, S.L., Khaneghah, E.M., Sharifi, M., Minaei-Bidgoli, B., Raahemi, B., Arab, M.N., Ardestani, A.S.: Four-dimensional model for describing the status of peers in peer-to-peer distributed systems. Turk. J. Electr. Eng. Comput. Sci. 21(6), 1646–1664 (2013)

    Google Scholar 

  • Mirtaheri, S.L., Khaneghah, E.M., Memaripour, A.S., Grandinetti, L., Sharifi, M., Bornaee, Z.: Multics and Plan 9: the big bangs in the distributed computing system universe. Comput. Sci. Eng. 16(5), 76–85 (2014)

    Article  Google Scholar 

  • Mirtaheri, S.L. et al.: A mathematical model for empowerment of Beowulf clusters for exascale computing. In: 2013 International Conference on High Performance Computing & Simulation (HPCS) (2013)

  • Mondal, R.K., et al.: Load balancing on selected nodes with average tasks in cloud computing. J. Innov. Electron. Commun. Eng. 6(2), 43–45 (2016)

    Google Scholar 

  • Mondal, R.K., Ray, P., Sarddar, D.: Load balancing. Int. J. Res. Comput. Appl. Inf. Technol. 4(1), 1–21 (2016)

    MathSciNet  Google Scholar 

  • Mondal, R.K. et al.: Load balancing with job switching in cloud computing network. In: Proceedings of the 5th International Conference on Frontiers in Intelligent Computing: Theory and Applications. Springer, Singapore (2017)

  • Mousavi Khaneghah, E., Noorabad Ghahroodi, R., Reyhani ShowkatAbad, A.: A mathematical multi-dimensional mechanism to improve process migration efficiency in peer-to-peer computing environments. Cogent Eng. 5(1), 1458434 (2018)

    Article  Google Scholar 

  • Mukherjee, D., Borst, S. C., Van Leeuwaarden, J. S. H., & Whiting, P. A. (2016, March). Efficient load balancing in large-scale systems. In Information Science and Systems (CISS), 2016 Annual Conference on (pp. 384–389). IEEE.

  • Pate Ahmadian, A., et al.: Resource discovery in non-structured peer to peer grid systems using the shuffled frog leaping algorithm. JTEC 10(4), 9–14 (2018)

    Google Scholar 

  • Pourqasem, J.: Toward the optimization resource discovery service in grid systems: a survey. J. Appl. Res. Ind. Eng. 5(4), 346–355 (2018)

    Google Scholar 

  • Qureshi, M.B., Dehnavi, M.M., Min-Allah, N., Qureshi, M.S., Hussain, H., Rentifis, I., et al.: Survey on grid resource allocation mechanisms. J. Grid Comput. 12(2), 399–441 (2014)

    Article  Google Scholar 

  • Ramezani, F., Lu, J., Hussain, F.K.: Task-based system load balancing in cloud computing using particle swarm optimization. Int. J. Parall. Progr. 42(5), 739–754 (2014)

    Article  Google Scholar 

  • Rao, A. et al.: Load balancing in structured P2P systems. In: International Workshop on Peer-to-Peer Systems. Springer, Berlin (2003)

  • Rathore, N.K., Chana, I.: Job migration policies for grid environment. Wirel. Pers. Commun. 89(1), 241–269 (2016)

    Article  Google Scholar 

  • Rathore, N.K., Rawat, U., Kulhari, S.C.: Efficient hybrid load balancing algorithm. Natl. Acad. Sci. Lett. 43(2), 177–185 (2020)

    Article  MathSciNet  Google Scholar 

  • Reylé, C., Richard, J., Cambrésy, L., Deleuil, M., Pécontal, E., Tresse, L.: Perspectives in numerical astrophysics: towards an exciting future in the exascale era. In: Proceedings of the Annual Meeting of the French Society of Astronomy & Astrophysics, pp. 133–137 (2016)

  • Shahrabi, Shirin et al. "Load Balancing in Distributed Exascale Computing Based on Process Requirements." Azerbaijan Journal of High Performance Computing, 2.1 (2018):158–167

  • Sharifi, M., Mirtaheri, S.L., Khaneghah, E.M.: A dynamic framework for integrated management of all types of resources in P2P systems. J. Supercomput. 52(2), 149–170 (2010)

    Article  Google Scholar 

  • Straatsma, T.P., Antypas, K.B., Williams, T.J.: Exascale Scientific Applications: Scalability and Performance Portability. Chapman and Hall/CRC, Boca Raton (2017)

    Book  Google Scholar 

  • Teylo, L., et al.: A hybrid evolutionary algorithm for task scheduling and data assignment of data-intensive scientific workflows on clouds. Future Gen. Comput. Syst. 76, 1–17 (2017)

    Article  Google Scholar 

  • Thakur, A., Goraya, M.S.: A taxonomic survey on load balancing in cloud. J. Netw. Comput. Appl. 98, 43–57 (2017)

    Article  Google Scholar 

  • van Steen, M., Tanenbaum, A.S.: A brief introduction to distributed systems. Computing 98(10), 967–1009 (2016)

    Article  MathSciNet  Google Scholar 

  • Wang, K. et al.: Optimizing load balancing and data-locality with data-aware scheduling. In: 2014 IEEE International Conference on Big Data (Big Data). IEEE (2014)

  • Wang, Ke et al. "Load‐balanced and locality‐aware scheduling for data‐intensive workloads at extreme scales." Concurrency and Computation: Practice and Experience 28.1 (2016a): 70–94.

  • Wang, K., et al.: Exploring the design tradeoffs for extreme-scale high-performance computing system software. IEEE Trans. Parall. Distrib. Syst. 27(4), 1070–1084 (2016b)

    Article  Google Scholar 

  • Wylie, B.J.N.: Exascale potholes for HPC: Execution performance and variability analysis of the flagship application code HemeLB. In: 2020 IEEE/ACM International Workshop on HPC User Support Tools (HUST) and Workshop on Programming and Performance Visualization Tools (ProTools). IEEE (2020)

Download references

Funding

The authors did not receive support from any organization for the submitted work.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ehsan Mousavi Khaneghah.

Ethics declarations

Conflict of interest

All authors certify that they have no affiliations with or involvement in any organization or entity with any financial or non-financial interest in the subject matter or materials discussed in this manuscript.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mollasalehi, F., Khaneghah, E.M., Showkatabadi, A.R. et al. ExaLB: a mathematical framework for load balancing to support distributed exascale computing environments. CCF Trans. HPC 5, 390–415 (2023). https://doi.org/10.1007/s42514-022-00134-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s42514-022-00134-8

Keywords