, Volume 98, Issue 5, pp 495–522 | Cite as

Multi-objective Swarm Intelligence schedulers for online scientific Clouds

  • Elina Pacini
  • Cristian Mateos
  • Carlos García Garino


Cloud Computing is a promising paradigm for parallel computing. However, as Cloud-based services become more dynamic, resource provisioning in Clouds becomes more challenging. The paradigm, with its promise of virtually infinite resources, seems to suit well in solving resource greedy scientific computing problems. In a Cloud, an appropriate number of Virtual Machines (VM) is created and allocated in physical resources for executing jobs. This work focuses on the Infrastructure as a Service (IaaS) model where custom VMs are launched in appropriate hosts available in a Cloud to execute scientific experiments coming from multiple users. Finding optimal solutions to allocate VMs to physical resources is an NP-complete problem, and therefore many heuristics have been developed. In this work, we describe and evaluate two Cloud schedulers based on Swarm Intelligence (SI) techniques, particularly Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO). The main performance metrics to study are the number of serviced users by the Cloud and the total number of created VMs in online (non-batch) scheduling scenarios. We also perform a sensitivity analysis by varying the specific-parameter values of each algorithm to evaluate the impact on the performance of the two objective metrics. The intra-Cloud network traffic is also measured. Simulated experiments performed using CloudSim and job data from real scientific problems show that the use of SI-based techniques succeeds in balancing the studied metrics compared to Genetic Algorithms.


Cloud Computing VM Scheduling Ant Colony Optimization  Particle Swarm Optimization Genetic Algorithms 

Mathematics Subject Classification

68M14 68M20 68T20 



We thank the anonymous reviewer for his/her constructive comments to improve the paper. We also thank Dr. David Monge for their insightful comments on scientific workflows. We acknowledge the financial support provided by ANPCyT through grants PAE-PICT 2007-02311, PAE-PICT 2007-02312, PICT-2012-0045, and UNCuyo University project 06/B253. The first author acknowledges her Ph.D. fellowships granted by the PRH-UNCuyo Project and the National Scientific and Technological Research Council (CONICET).


  1. 1.
    Agostinho L, Feliciano G, Olivi L, Cardozo E, Guimaraes E (2011) A bio-inspired approach to provisioning of virtual resources in federated Clouds. In: Ninth International conference on dependable, autonomic and secure computing (DASC), DASC 11. IEEE Computer Socienty, Washington, DC, USA, pp 598–604Google Scholar
  2. 2.
    Alfano G, Angelis FD, Rosati L (2001) General solution procedures in elasto-viscoplasticity. Comput Methods Appl Mech Eng 190(39):5123–5147CrossRefzbMATHGoogle Scholar
  3. 3.
    Banerjee S, Mukherjee I, Mahanti P (2009) Cloud computing initiative using modified ant colony framework. In: World Academy of Science, Engineering and Technology, WASET, pp 221–224Google Scholar
  4. 4.
    Bonabeau E, Dorigo M, Theraulaz G (1999) Swarm intelligence: from natural to artificial systems. Oxford University Press, New YorkzbMATHGoogle Scholar
  5. 5.
    Buyya R, Yeo C, Venugopal S, Broberg J, Brandic I (2009) Cloud computing and emerging IT platforms: vision, hype, and reality for delivering computing as the 5th utility. Future Gener Comput Syst 25(6):599–616CrossRefGoogle Scholar
  6. 6.
    Calheiros R, Ranjan R, Beloglazov A, De Rose C, Buyya R (2011) CloudSim: a toolkit for modeling and simulation of Cloud Computing environments and evaluation of resource provisioning algorithms. Softw Pract Exp 41(1):23–50CrossRefGoogle Scholar
  7. 7.
    Careglio C, Monge D, Pacini E, Mateos C, Mirasso A, García Garino C (2010) Sensibilidad de resultados del ensayo de tracción simple frente a diferentes tamaños y tipos de imperfecciones. In: Dvorkin MGE, Storti M (eds) Mecánica Computacional, vol XXIX. AMCA, pp 4181–4197Google Scholar
  8. 8.
    Celesti A, Fazio M, Villari M, Puliafito A (2012) Virtual machine provisioning through satellite communications in federated Cloud environments. Future Gener Comput Syst 28(1):85–93CrossRefGoogle Scholar
  9. 9.
    Deelman E, Blythe J, Gil Y, Kesselman C, Mehta G, Patil S, Su M, Vahi K, Livny M (2004) Pegasus: mapping scientific workflows onto the grid. In: Dikaiakos M (ed) Grid computing. Lecture Notes in Computer Science, vol 3165. Springer, Berlin, pp 11–20Google Scholar
  10. 10.
    Deelman E, Gannon D, Shields M, Taylor I (2009) Workflows and e-Science: an overview of workflow system features and capabilities. Future Gener Comput Syst 25(5):528–540CrossRefGoogle Scholar
  11. 11.
    Dhinesh Babu L, Venkata Krishna P (2013) Honey bee behavior inspired load balancing of tasks in cloud computing environments. Appl Soft Comput 13(5):2292–2303CrossRefGoogle Scholar
  12. 12.
    Dorigo M (1992) Optimization, learning and natural algorithms. Phdthesis, Politecnico di Milano, Milano, ItalyGoogle Scholar
  13. 13.
    Dorigo M, Stützle T (2003) The ant colony optimization metaheuristic: algorithms, applications, and advances. In: Glover F, Kochenberger G (eds) Handbook of metaheuristics, international series in operations research & management science, vol. 57, chap. 9. Springer, New York, pp 250–285Google Scholar
  14. 14.
    Farmahini-Farahani A, Vakili S, Fakhraie S, Safari S, Lucas C (2010) Parallel scalable hardware implementation of asynchronous discrete Particle Swarm Optimization. Eng Appl Artif Intell 23(2):177–187CrossRefGoogle Scholar
  15. 15.
    Garino García C, Gabaldón F, Goicolea JM (2006) Finite element simulation of the simple tension test in metals. Finite Elem Anal Des 42(13):1187–1197CrossRefGoogle Scholar
  16. 16.
    García Garino C, Mateos C, Pacini E (2012) Job scheduling of parametric computational mechanics studies on cloud computing infrastructures. In: International advanced research workshop on high performance computing, grid and clouds. Cetraro (Italy).
  17. 17.
    Garino García C, Pacini E, Monge D, Careglio C, Mirasso A (2013) Computational mechanics software as a service project. J Comput Sci Technol 13(3):160–166Google Scholar
  18. 18.
    García Garino C, Ribero Vairo M, ía Fagés S, Mirasso A, Ponthot JP (2013) Numerical simulation of finite strain viscoplastic problems. J Comput Appl Math 246:174–184MathSciNetCrossRefzbMATHGoogle Scholar
  19. 19.
    Huang L, Chen H, Hu T (2013) Survey on resource allocation policy and job scheduling algorithms of cloud computing. J Softw 8(2):480–487CrossRefGoogle Scholar
  20. 20.
    Kennedy J, Eberhart R (1995) Particle Swarm Optimization. In: IEEE international conference on neural networks, vol 4. IEEE Computer Society, pp 1942–1948Google Scholar
  21. 21.
    Ktari R, Chabchoub H (2013) Essential Particle Swarm Optimization queen with Tabu Search for MKP resolution. Computing (in press)Google Scholar
  22. 22.
    Liu J, Guo Luo X, Zhang XMF (2013) Job scheduling algorithm for Cloud Computing based on Particle Swarm Optimization. Adv Mater Res 662:957–960CrossRefGoogle Scholar
  23. 23.
    Lucas-Simarro J, Moreno-Vozmediano R, Montero R, Llorente I (2013) Scheduling strategies for optimal service deployment across multiple clouds. Future Gener Comput Syst 29(6):1431–1441 (including special sections: high performance computing in the cloud & resource discovery mechanisms for P2P systems)Google Scholar
  24. 24.
    Ludwig S, Moallem A (2011) Swarm intelligence approaches for grid load balancing. J Grid Comput 9(3):279–301CrossRefGoogle Scholar
  25. 25.
    Mateos C, Pacini E, García Garino C (2013) An ACO-inspired algorithm for minimizing weighted flowtime in cloud-based parameter sweep experiments. Adv Eng Softw 56:38–50CrossRefGoogle Scholar
  26. 26.
    Monge D, García Garino C (2014) LOGOS: enabling local resource managers for the efficient support of data-intensive workflows within grid sites. Comput Inform 33(1) (in press)Google Scholar
  27. 27.
    Moreno Vozmediano R, Montero R, Llorente I (2012) IaaS Cloud architecture: from virtualized datacenters to federated Cloud infrastructures. IEEE Comput 45(12):65–72CrossRefGoogle Scholar
  28. 28.
    Pacini E, Mateos C, García Garino C (2013) Dynamic scheduling of scientific experiments on clouds using Ant Colony Optimization. In: Topping BHV, Iványi P (eds) Proceedings of the third international conference on parallel, distributed, grid and cloud computing for engineering. Civil-Comp Press, Stirlingshire, UK. Paper 33
  29. 29.
    Pacini E, Mateos C, García Garino C (2014) Distributed job scheduling based on Swarm Intelligence: a survey. Comput Electr Eng 40(1):252–269 (40th-year commemorative issue)Google Scholar
  30. 30.
    Pacini E, Ribero M, Mateos C, Mirasso A, García Garino C (2011) Simulation on cloud computing infrastructures of parametric studies of nonlinear solids problems. In: Cipolla-Ficarra FV et al. (ed) Advances in new technologies, interactive interfaces and communicability (ADNTIIC 2011), LNCS, vol 7547. Springer, Berlin, pp 58–70Google Scholar
  31. 31.
    Palmieri F, Buonanno L, Venticinque S, Aversa R, Martino BD (2013) A distributed scheduling framework based on selfish autonomous agents for federated cloud environments. Future Gener Comput Syst 29(6):1461–1472CrossRefGoogle Scholar
  32. 32.
    Pedemonte M, Nesmachnow S, Cancela H (2011) A survey on parallel ant colony optimization. Appl Soft Comput 11(8):5181–5197CrossRefGoogle Scholar
  33. 33.
    Poli R (2008) Analysis of the publications on the applications of particle swarm optimisation. J Artif Evol Appl 4:1–10Google Scholar
  34. 34.
    Tavares Neto R, Godinho Filho M (2013) Literature review regarding Ant Colony Optimization applied to scheduling problems: guidelines for implementation and directions for future research. Eng Appl Artif Intell 26(1):150–161CrossRefGoogle Scholar
  35. 35.
    Tordsson J, Montero R, Moreno-Vozmediano R, Llorente I (2012) Cloud brokering mechanisms for optimized placement of virtual machines across multiple providers. Future Gener Comput Syst 28(2):358–367CrossRefGoogle Scholar
  36. 36.
    Wang L, Cui Y, Stojmenovic I, Ma X, Song J (2013) Energy efficiency on location based applications in mobile cloud computing: a survey. Computing (in press)Google Scholar
  37. 37.
    Woeginger G (2003) Exact algorithms for NP-hard problems: a survey. In: Junger M, Reinelt G, Rinaldi G (eds) Combinatorial optimization—Eureka, You Shrink!. Lecture Notes in Computer Science, vol 2570. Springer, Berlin, pp 185–207Google Scholar
  38. 38.
    Xhafa F, Abraham A (2010) Computational models and heuristic methods for grid scheduling problems. Future Gener Comput Syst 26(4):608–621. doi: 10.1016/j.future.2009.11.005
  39. 39.
    Zehua Z, Xuejie Z (2010) A load balancing mechanism based on ant colony and complex network theory in open Cloud Computing federation. In: 2nd international conference on industrial mechatronics and automation. IEEE Computer Socienty, pp 240–243Google Scholar
  40. 40.
    Zhan S, Huo H (2012) Improved PSO-based Task Scheduling Algorithm in Cloud Computing. J Inf Comput Sci 9(13):3821–3829Google Scholar

Copyright information

© Springer-Verlag Wien 2014

Authors and Affiliations

  • Elina Pacini
    • 1
  • Cristian Mateos
    • 2
  • Carlos García Garino
    • 3
  1. 1.ITIC-UNCuyo UniversityMendozaArgentina
  2. 2.ISISTAN Research Institute-UNICEN UniversityTandilArgentina
  3. 3.ITIC and Facultad de Ingeniería-UNCuyo UniversityMendozaArgentina

Personalised recommendations