Journal of Network and Systems Management

, Volume 27, Issue 3, pp 688–729 | Cite as

A Bio-inspired Datacenter Selection Scheduler for Federated Clouds and Its Application to Frost Prediction

  • Elina PaciniEmail author
  • Lucas Iacono
  • Cristian Mateos
  • Carlos García Garino


Frost is an agro-meteorological event which causes both damage in crops and important economic losses, therefore frost prediction applications (FPA) are very important to help farmers to mitigate possible damages. FPA involves the execution of many CPU-intensive jobs. This work focuses on efficiently running FPAs in paid federated Clouds, where custom virtual machines (VM) are launched in appropriate resources belonging to different providers. The goal of this work is to minimize both the makespan and monetary cost. We follow a federated Cloud model where scheduling is performed at three levels. First, at the broker level, a datacenter is selected taking into account certain criteria established by the user, such as lower costs or lower latencies. Second, at the infrastructure level, a specialized scheduler is responsible for mapping VMs to datacenter hosts. Finally, at the VM level, jobs are assigned for execution into the preallocated VMs. Our proposal mainly contributes to implementing bio-inspired strategies at two levels. Specifically, two broker-level schedulers based on Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO), which aim to select the datacenters taking into account the network latencies, monetary cost and the availability of computational resources in datacenters, are implemented. Then, VMs are allocated in the physical machines of that datacenter by another intra-datacenter scheduler also based on ACO and PSO. Performed experiments show that our bio-inspired scheduler succeed in reducing both the makespan and the monetary cost with average gains of around 50% compared to genetic algorithms.


Scientific computing Frost prediction applications Cloud computing Scheduling Ant colony optimization Particle Swarm optimization Genetic algorithms 



We acknowledge the financial support provided by ANPCyT through grants PICT-2012-2731, PICT-2014-1430 and PICT-2015-1435, and UNCuyo project project 06/B308. We want to thank the anonymous reviewers for their valuable comments and suggestions that helped to improve the quality of this paper.


  1. 1.
    Snyder, R.L., de Melo-Abreu, J.P.: Frost Protection: Fundamentals, Practice and Economics, Volume 1 of Environment and Natural Resources Series. Food and Agriculture Organization of the United Nations (FAO), Rome (2005)Google Scholar
  2. 2.
    Bishop, C.: Pattern Recognition and Machine Learning, Volume 20 of Information Science and Statistics. Springer, Berlin (2006)Google Scholar
  3. 3.
    Oliveira, L., Rodrigues, J.: Wireless sensor networks: a survey on environmental monitoring. J. Commun. 6(2), 143–151 (2011)CrossRefGoogle Scholar
  4. 4.
    Rehman, A., Abbasi, A.Z., Islam, N., Shaikh, Z.A.: A review of wireless sensors and networks’ applications in agriculture. Comput. Stand. Interfaces 36(2), 263–270 (2014)CrossRefGoogle Scholar
  5. 5.
    Buyya, R., Yeo, C., Venugopal, S., Broberg, J., Brandic, I.: Cloud computing and emerging IT platforms: vision, hype, and reality for delivering computing as the 5th utility. Future Gener. Comput. Syst. 25(6), 599–616 (2009)CrossRefGoogle Scholar
  6. 6.
    Mauch, V., Kunze, M., Hillenbrand, M.: High performance cloud computing. Future Gener. Comput. Syst. 29(6), 1408–1416 (2013) (Including Special sections: High Performance Computing in the Cloud & Resource Discovery Mechanisms for P2P Systems) CrossRefGoogle Scholar
  7. 7.
    Zhai, Y., Liu, M., Zhai, J., Ma, X., Chen, W.: Cloud versus in-house cluster: evaluating Amazon cluster compute instances for running mpi applications. In: State of the Practice Reports, vol. 11, pp. 1–11. ACM (2011)Google Scholar
  8. 8.
    Coutinho, R.C., Drummond, L.M., Frota, Y., de Oliveira, D.: Optimizing virtual machine allocation for parallel scientific workflows in federated clouds. Future Gener. Comput. Syst. 46, 51–68 (2014)CrossRefGoogle Scholar
  9. 9.
    Petri, I., Beach, T., Mengsong, Z., Montes, J.D., Rana, O., Parashar, M.: Exploring models and mechanisms for exchanging resources in a federated cloud. In: IEEE International Conference on Cloud Engineering (IC2E), pp. 215–224. IEEE (2014)Google Scholar
  10. 10.
    Celesti, A., Fazio, M., Villari, M., Puliafito, A.: Virtual machine provisioning through satellite communications in federated cloud environments. Future Gener. Comput. Syst. 28(1), 85–93 (2012)CrossRefGoogle Scholar
  11. 11.
    Pacini, E., Mateos, C., García Garino, C., Careglio, C., Mirasso, A.: A bio-inspired scheduler for minimizing makespan and flowtime of computational mechanics applications on federated clouds. J. Intell. Fuzzy Syst. 31(3), 1731–1743 (2016)CrossRefGoogle Scholar
  12. 12.
    Manasrah, A.M., Smadi, T., ALmomani, A.: A variable service broker routing policy for data center selection in cloud analyst. J. King Saud Univ. Comput. Inf. Sci. 29(3), 365–377 (2017)Google Scholar
  13. 13.
    Woeginger, G.: Exact algorithms for NP-Hard problems: a survey. In: Junger, M., Reinelt, G., Rinaldi, G. (eds.) Combinatorial Optimization—Eureka, You Shrink!, volume 2570 of Lecture Notes in Computer Science, pp. 185–207. Springer (2003)Google Scholar
  14. 14.
    Kennedy, J.: Swarm Intelligence. In: Zomaya, Albert Y. (ed.) Handbook of Nature-Inspired and Innovative Computing, pp. 187–219. Springer, New York (2006)CrossRefGoogle Scholar
  15. 15.
    Pacini, E., Mateos, C., García Garino, C.: Distributed job scheduling based on Swarm Intelligence: a survey. Comput. Electr. Eng. 40(1), 252–269 (2014). 40th-year commemorative issueCrossRefGoogle Scholar
  16. 16.
    Pacini, E., Mateos, C., García Garino, C.: Balancing throughput and response time in online scientific clouds via ant colony optimization. Adv. Eng. Softw. 84, 31–47 (2015)CrossRefGoogle Scholar
  17. 17.
    Pacini, E., Mateos, C., García Garino, C.: SI-based scheduling of parameter sweep experiments on federated clouds. In: Hernandez, G., et. al. (eds.) First HPCLATAM—CLCAR Joint Conference Latin American High Performance Computing Conference (CARLA), volume 845 of High Performance Computing. Communications in Computer and Information Science, pp. 28–42. Springer (2014)Google Scholar
  18. 18.
    Pacini, E., Mateos, C., García Garino, C.: A three-level scheduler to execute scientific experiments on federated clouds. IEEE Latin Am. Trans. 13(10), 3359–3369 (2015)CrossRefGoogle Scholar
  19. 19.
    Armbrust, M., Fox, A., Griffith, R., Joseph, A.D., Katz, R., Konwinski, A., Lee, G., Patterson, D., Rabkin, A., Stoica, I., Zaharia, M.: A view of cloud computing. Commun. ACM 53(4), 50–58 (2010)CrossRefGoogle Scholar
  20. 20.
    Iosup, A., Ostermann, S., Yigitbasi, N., Prodan, R., Fahringer, T., Epema, D.: Performance analysis of cloud computing services for many-tasks scientific computing. IEEE Trans. Parallel Distrib. Syst. 22(6), 931–945 (2011)CrossRefGoogle Scholar
  21. 21.
    Calheiros, R., Ranjan, R., Beloglazov, A., De Rose, C., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Softw. Pract. Exp. 41(1), 23–50 (2011)CrossRefGoogle Scholar
  22. 22.
    Pacini, E., Mateos, C., García Garino, C.: Multi-objective Swarm Intelligence schedulers for online scientific clouds. Special Issue on Cloud Computing. Computing, pp. 1–28 (2014)Google Scholar
  23. 23.
    Agostinho, L., Feliciano, G., Olivi, L., Cardozo, E., Guimaraes, E.: 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, pp. 598–604, Washington, DC, USA, 12–14 December 2011. IEEE Computer Socienty (2011)Google Scholar
  24. 24.
    Chandra Mohan, B., Baskaran, R.: A survey: ant colony optimization based recent research and implementation on several engineering domain. Expert Syst. Appl. 39(4), 4618–4627 (2012)CrossRefGoogle Scholar
  25. 25.
    Mahdiyeh, E., Hussain, S., Mohammad, K., Azah, M.: A survey of the state of the art in Particle Swarm Optimization. Res. J. Appl. Sci. Eng. Technol. 4(9), 1181–1197 (2012)Google Scholar
  26. 26.
    Pedemonte, M., Nesmachnow, S., Cancela, H.: A survey on parallel Ant Colony Optimization. Appl. Soft Comput. 11(8), 5181–5197 (2011)CrossRefGoogle Scholar
  27. 27.
    Poli, R.: Analysis of the publications on the applications of Particle Swarm Optimisation. J. Artif. Evol. Appl. 2008(4), 1–10 (2008)Google Scholar
  28. 28.
    Tavares Neto, R.F., Godinho Filho, M.: Literature review regarding Ant Colony Optimization applied to scheduling problems: guidelines for implementation and directions for future research. Eng. Appl. Artif. Intell. 26(1), 150–161 (2013)CrossRefGoogle Scholar
  29. 29.
    Vlachos, A.: Ant colony system algorithm solving a thermal generator maintenance scheduling problem. J. Intell. Fuzzy Syst. 24(4), 713–723 (2013)CrossRefGoogle Scholar
  30. 30.
    Masdari, M., Salehi, F., Jalali, M., Bidaki, M.: A survey of PSO-based scheduling algorithms in cloud computing. J. Netw. Syst. Manag. 25(1), 122–158 (2017)CrossRefGoogle Scholar
  31. 31.
    Singha, U., Jain, S.: An analysis of swarm intelligence based load balancing algorithms in a cloud computing environment. Int. J. Hybrid Inf. Technol 8(1), 249–256 (2015)CrossRefGoogle Scholar
  32. 32.
    Zhan, Z.H., Liu, X.F., Gong, Y.J., Zhang, J., Chung, H.S.H., Li, Y.: Cloud computing resource scheduling and a survey of its evolutionary approaches. ACM Comput. Surv. 47(4), 63:1–63:33 (2015)CrossRefGoogle Scholar
  33. 33.
    Marosi, A.C., Kecskemeti, G., Kertesz, A., Kacsuk, P.: Fcm: anarchitecture for integrating iaas cloud systems. In: Cloud computing 2011: the second international conference on cloud computing, GRIDs, and virtualization, pp. 7–12. IARIA (2011)Google Scholar
  34. 34.
    Villegas, D., Bobroff, N., Rodero, I., Delgado, J., Liu, Y., Devarakonda, A., Fong, L., Sadjadi, S.M., Parashar, M.: Cloud federation in a layered service model. J. Comput. Syst. Sci. 78(5), 1330–1344 (2012). JCSS Special Issue: Cloud Computing 2011CrossRefGoogle Scholar
  35. 35.
    Tordsson, J., Montero, R.S., Moreno Vozmediano, Rl, Llorente, I.M.: Cloud brokering mechanisms for optimized placement of virtual machines across multiple providers. Future Gener. Comput. Syst. 28(2), 358–367 (2012)CrossRefGoogle Scholar
  36. 36.
    Kessaci, Y., Melab, N., Talbi, E.-G.: A pareto-based metaheuristic for scheduling HPC applications on a geographically distributed cloud federation. Cluster Comput. 16(3), 451–468 (2013)CrossRefGoogle Scholar
  37. 37.
    Lucas-Simarro, J.L., Moreno-Vozmediano, R., Montero, R.S., Llorente, I.M.: Scheduling strategies for optimal service deployment across multiple clouds. Future Gener. Comput. Syst. 29(6), 1431–1441 (2013) (Including Special sections: High Performance Computing in the Cloud & Resource Discovery Mechanisms for P2P Systems) CrossRefGoogle Scholar
  38. 38.
    Song, Y., Peng, J., Liu, K., Jiang, F., Liu, W., Huang, Z.: A hybrid particle swarm ant colony based resource reservation for geo-distributed cloud service. In: 2016 IEEE Global Communications Conference (GLOBECOM), pp 1–6. IEEE (2016)Google Scholar
  39. 39.
    Kumrai, T., Ota, K., Dong, M., Kishigami, J., Sung, D.K.: Multiobjective optimization in cloud brokering systems for connected internet of things. IEEE Internet Things J. 4(2), 404–413 (2016)CrossRefGoogle Scholar
  40. 40.
    Feller, E., Rilling, L., Morin, C.: Energy-Aware Ant Colony based workload placement in clouds. In: 12th International Conference on Grid Computing, number 8 in Grid ’11, pp. 26–33. IEEE Computer Society (2011)Google Scholar
  41. 41.
    Gao, Y., Guan, H., Qi, Z., Hou, Y., Liu, L.: A multi-objective ant colony system algorithm for virtual machine placement in cloud computing. J. Comput. Syst. Sci. 79(8), 1230–1242 (2013)MathSciNetzbMATHCrossRefGoogle Scholar
  42. 42.
    Jeyarani, R., Nagaveni, N., Vasanth Ram, R.: Design and implementation of adaptive power-aware virtual machine provisioner (APA-VMP) using swarm intelligence. Future Gener. Comput. Syst. 28(5), 811–821 (2012)CrossRefGoogle Scholar
  43. 43.
    Dhinesh Babu, L.D., Venkata Krishna, P.: Honey bee behavior inspired load balancing of tasks in cloud computing environments. Appl. Soft Comput. 13(5), 2292–2303 (2013)CrossRefGoogle Scholar
  44. 44.
    de Oliveira, G.S., Ribeiro, E., Ferreira, D.A., Araújo, A.P.,Holanda, M., Walter, M.E.: ACOsched: a scheduling algorithm in afederated Cloud infrastructure for bioinformatics applications. In: International Conference on Bioinformatics and Biomedicine, pp. 8–14. IEEE (2013)Google Scholar
  45. 45.
    Zhang, G., Zuo, X.: Deadline constrained task scheduling based on standard-pso in a hybrid cloud. In: Tan, Y., Shi, Y., Mo, H. (eds.) Advances in Swarm Intelligence: 4th International Conference, ICSI 2013, pp. 200–209, Harbin, China. Springer, Berlin (2013)Google Scholar
  46. 46.
    Gabaldon, E., Vila, S., Guirado, F., Lerida, J.L., Planes, J.: Energy efficient scheduling on heterogeneous federated clusters using a fuzzy multi-objective meta-heuristic. In: IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp. 1–6 (2017)Google Scholar
  47. 47.
    Sedeño Noda, A., Raith, A.: A dijkstra-like method computing all extreme supported non-dominated solutions of the biobjective shortest path problem. Comput. Oper. Res. 57, 83–94 (2015)MathSciNetzbMATHCrossRefGoogle Scholar
  48. 48.
    Breque, F., Nemer, M.: Frosting modeling on a cold flat plate: comparison of the different assumptions and impacts on frost growth predictions. Int. J. Refrig. 69, 340–360 (2016)CrossRefGoogle Scholar
  49. 49.
    Brun Laguna, K., Diedrichs, A.L., Chaar, J.E., Dujovne, D., Taffernaberry, J.C., Mercado, G., Watteyne, T.: A demo of the peach iot-based frost event prediction system for precision agriculture. In: 2016 13th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON), pp. 1–3. IEEE (2016)Google Scholar
  50. 50.
    Iacono, L., Vázquez-Poletti, J.L., García Garino, C., Llorente, I.M.: A Model to Calculate Amazon EC2 Instance Performance in Frost Prediction Applications. In: Hernández, G., et al. (eds.) High Performance Computing: First HPCLATAM—CLCAR Latin American Joint Conference, CARLA 2014, pp. 68–82. Springer, Berlin (2014)CrossRefGoogle Scholar
  51. 51.
    Iacono, L., Vázquez-Poletti, J.L., García Garino, C., Llorente, I.M.: A performance models for frost prediction on public cloud infrastructures. Comput. Inf. 37(4), 815–837 (2018)Google Scholar
  52. 52.
    Monge, D.A., Pacini, E., Mateos, C., García Garino, C.: Meta-heuristic based autoscaling of cloud-based parameter sweep experiments with unreliable virtual machines instances. Comput. Electr. Eng. 69, 364–377 (2018)CrossRefGoogle Scholar
  53. 53.
    Jung, J.K., Jung, S.M., Kim, T.K., Chung, T.M.: A study on the cloud simulation with a network topology generator. World Acad. Sci. Eng. Technol. 6(11), 303–306 (2012)Google Scholar
  54. 54.
    Madivi, R., Kamath, S.S.: An hybrid bio-inspired task scheduling algorithm in cloud environment. In Fifth International Conference on Computing, Communications and Networking Technologies (ICCCNT), pp. 1–7. IEEE (2014)Google Scholar
  55. 55.
    Karaboga, D., Gorkemli, B., Ozturk, C., Karaboga, N.: A comprehensive survey: artificial bee colony (ABC) algorithm and applications. Artif. Intell. Rev. (2012)Google Scholar
  56. 56.
    Ghafarian, T., Javadi, B.: Cloud-aware data intensive workflow scheduling on volunteer computing systems. Future Gener. Comput. Syst. 51, 87–97 (2015)CrossRefGoogle Scholar
  57. 57.
    Zhao, Y., Li, Y., Raicu, L., Lu, S., Tian, W., Liu, H.: Enabling scalable scientific workflow management in the cloud. Future Gener. Comput. Syst. 46, 3–16 (2015)CrossRefGoogle Scholar
  58. 58.
    Philip Chen, C.L., Zhang, C.Y.: Data-intensive applications, challenges, techniques and technologies: a survey on big data. Inf. Sci. 275, 314–347 (2014)CrossRefGoogle Scholar
  59. 59.
    Neshat, M., Sepidnam, G., Sargolzaei, M., Toosi, A.N.: Artificial fish swarm algorithm: a survey of the state-of-the-art, hybridization, combinatorial and indicative applications. Artif. Intell. Rev. 42(4), 965–997 (2014)CrossRefGoogle Scholar
  60. 60.
    Zhou, A., Wang, S., Yang, C., Sun, L., Sun, Q., Yang, F.: Ftcloudsim: support for cloud service reliability enhancement simulation. Int. J. Web Grid Serv. 11(4), 347–361 (2015)CrossRefGoogle Scholar
  61. 61.
    Bonabeau, E., Dorigo, M., Theraulaz, G.: Swarm Intelligence: From Natural to Artificial Systems. Oxford University Press, Oxford (1999)zbMATHGoogle Scholar
  62. 62.
    Dorigo, M., Birattari, M.: Swarm intelligence. Scholarpedia 2(9), 1462 (2007)CrossRefGoogle Scholar
  63. 63.
    Dorigo, M., Stützle, T.: The ant colony optimization metaheuristic: algorithms, applications, and advances. In: Handbook of Metaheuristics, volume 57 of International Series in Operations Research and Management Science, chapter 9, pp. 250–285. Springer (2003)Google Scholar
  64. 64.
    Kennedy, J., Eberhart, R.: Swarm Intelligence. Morgan Kaufmann Publishers Inc., San Francisco (2001)Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.ITIC and Facultad de IngenieríaUNCuyo UniversityMendozaArgentina
  2. 2.CONICETBuenos AiresArgentina
  3. 3.ISISTAN-CONICETUNICEN UniversityTandilArgentina

Personalised recommendations