Dependable and Coordinated Resources Allocation Algorithms for Distributed Computing

  • Victor ToporkovEmail author
  • Dmitry Yemelyanov
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 965)


In this work, we introduce slot selection and co-allocation algorithms for parallel jobs in distributed computing with non-dedicated and heterogeneous resources. A single slot is a time span that can be assigned to a task, which is a part of a parallel job. The job launch requires a co-allocation of a specified number of slots starting and finishing synchronously. Some existing resource co-allocation algorithms assign a job to the first set of slots matching the resource request without any optimization (the first fit type), while other algorithms are based on an exhaustive search. In this paper, algorithms for efficient, dependable and coordinated slot selection are studied and compared with known approaches. The novelty of the proposed approach is in a general algorithm efficiently selecting a set of slots according to the specified criterion.


Distributed computing Grid Dependability Coordinated scheduling Resource management Slot Job Allocation Optimization 



This work was partially supported by the Council on Grants of the President of the Russian Federation for State Support of Young Scientists (YPhD-2297.2017.9), RFBR (grants 18-07-00456 and 18-07-00534) and by the Ministry on Education and Science of the Russian Federation (project no. 2.9606.2017/8.9).


  1. 1.
    Dimitriadou, S.K., Karatza, H.D.: Job scheduling in a distributed system using backfilling with inaccurate runtime computations. In: Proceedings of 2010 International Conference on Complex, Intelligent and Software Intensive Systems, pp. 329–336 (2010)Google Scholar
  2. 2.
    Toporkov, V., Toporkova, A., Tselishchev, A., Yemelyanov, D., Potekhin, P.: Heuristic strategies for preference-based scheduling in virtual organizations of utility grids. J. Ambient Intell. Hum. Comput. 6(6), 733–740 (2015)CrossRefGoogle Scholar
  3. 3.
    Buyya, R., Abramson, D., Giddy, J.: Economic models for resource management and scheduling in grid computing. J. Concurr. Comput. Pract. Exp. 5(14), 1507–1542 (2002)CrossRefGoogle Scholar
  4. 4.
    Foster, I., Kesselman, C., Tuecke, S.: The anatomy of the grid: enabling scalable virtual organizations. Int. J. High Perform. Comput. Appl. 15(3), 200–222 (2001)CrossRefGoogle Scholar
  5. 5.
    Carroll, T., Grosu, D.: Formation of virtual organizations in grids: a game-theoretic approach. Econ. Mod. Algorithms Distrib. Syst. 22(14), 63–81 (2009)CrossRefGoogle Scholar
  6. 6.
    Yang, R., Xu, J.: Computing at massive scale: scalability and dependability challenges. In: 2016 IEEE Symposium on Service-Oriented System Engineering (SOSE), pp. 386–397 (2016)Google Scholar
  7. 7.
    Ernemann, C., Hamscher, V., Yahyapour, R.: Economic scheduling in grid computing. In: Feitelson, Dror G., Rudolph, L., Schwiegelshohn, U. (eds.) JSSPP 2002. LNCS, vol. 2537, pp. 128–152. Springer, Heidelberg (2002). Scholar
  8. 8.
    Aida, K., Casanova, H.: Scheduling mixed-parallel applications with advance reservations. In: 17th IEEE International Symposium on HPDC, pp. 65–74. IEEE CS Press, New York (2008)Google Scholar
  9. 9.
    Elmroth, E., Tordsson, J.: A standards-based grid resource brokering service supporting advance reservations, co-allocation and cross-grid interoperability. J. Concurr. Comput. Pract. Exp. 25(18), 2298–2335 (2009)CrossRefGoogle Scholar
  10. 10.
    Takefusa, A., Nakada, H., Kudoh, T., Tanaka, Y.: An advance reservation-based co-allocation algorithm for distributed computers and network bandwidth on QoS-guaranteed grids. In: Frachtenberg, E., Schwiegelshohn, U. (eds.) JSSPP 2010. LNCS, vol. 6253, pp. 16–34. Springer, Heidelberg (2010). Scholar
  11. 11.
    Blanco, H., Guirado, F., Lérida, J.L., Albornoz, V.M.: MIP model scheduling for multi-clusters. In: Caragiannis, I., et al. (eds.) Euro-Par 2012. LNCS, vol. 7640, pp. 196–206. Springer, Heidelberg (2013). Scholar
  12. 12.
    Garg, S.K., Konugurthi, P., Buyya, R.: A linear programming-driven genetic algorithm for meta-scheduling on utility grids. Int. J. Parallel Emergent Distrib. Syst. 26, 493–517 (2011)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Moab Adaptive Computing. Accessed 12 Apr 2018
  14. 14.
    Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A.F., Buyya, R.: CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. J. Softw. Pract. Exp. 41(1), 23–50 (2011)CrossRefGoogle Scholar
  15. 15.
    Samimi, P., Teimouri, Y., Mukhtar, M.: A combinatorial double auction resource allocation model in cloud computing. J. Inf. Sci. 357 C, 201–216 (2016)CrossRefGoogle Scholar
  16. 16.
    Toporkov, V., Toporkova, A., Bobchenkov, A., Yemelyanov, D.: Resource selection algorithms for economic scheduling in distributed systems. In: Proceedings of International Conference on Computational Science, ICCS 2011, 1–3 June 2011, Singapore, Procedia Computer Science, vol. 4, pp. 2267–2276. Elsevier (2011)Google Scholar
  17. 17.
    Kovalenko, V.N., Koryagin, D.A.: The grid: analysis of basic principles and ways of application. J. Programm. Comput. Softw. 35(1), 18–34 (2009)CrossRefGoogle Scholar
  18. 18.
    Makhlouf, S., Yagoubi, B.: Resources co-allocation strategies in grid computing. In: CIIA, CEUR Workshop Proceedings, vol. 825 (2011)Google Scholar
  19. 19.
    Netto, M.A.S., Buyya, R.: A flexible resource co-allocation model based on advance reservations with rescheduling support. In: Technical Report, GRIDSTR-2007-17, Grid Computing and Distributed Systems Laboratory, The University of Melbourne, Australia, 9 October 2007Google Scholar
  20. 20.
    Toporkov, V., Toporkova, A., Tselishchev, A., Yemelyanov, D.: Slot selection algorithms in distributed computing. J. Supercomput. 69(1), 53–60 (2014)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.National Research University “MPEI”MoscowRussia

Personalised recommendations