Journal of Grid Computing

, Volume 14, Issue 2, pp 193–216 | Cite as

An Analysis of Public Clouds Elasticity in the Execution of Scientific Applications: a Survey

  • Guilherme Galante
  • Luis Carlos Erpen De Bona
  • Antonio Roberto Mury
  • Bruno Schulze
  • Rodrigo da Rosa Righi


Elasticity can be seen as the ability of a system to increase or decrease the computing resources allocated in a dynamic and on demand way. It is an important feature provided by cloud computing, that has been widely used in web applications and is also gaining attention in the scientific community. Considering the possibilities of using elasticity in this context, a question arises: “Are the available public cloud solutions suitable to provide elasticity to scientific applications?” To answer the question, in a first moment we present a survey on the use of cloud computing in scientific scenarios, providing an overview of the subject. Next, we describe the elasticity mechanisms offered by major public cloud providers and analyzes the limitations of the solutions in providing elasticity for scientific applications. As the main contribution of the article, we also present an analysis over some initiatives that are being developed to overcome the current challenges. In our opinion, current computational clouds are developing rapidly but have not yet reached the necessary maturity level to meet all scientific applications elasticity requirements. We expect that in the coming years the efforts being taken by numerous researchers in this area identify and address these challenges and lead to better and more mature technologies that will improve cloud computing practices.


Cloud computing Elasticity Scientific applications 


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  1. 1.
    Villamizar, M., Castro, H., Mendez, D.: E-Clouds: a Saas Marketplace for Scientific Computing. In: Proceedings IEEE/ACM 5th International Conference on Utility and Cloud Computing, pp 13–20. IEEE (2012)Google Scholar
  2. 2.
    Simmhan, Y., Van Ingen, C., Subramanian, G., Li, J.: Bridging the Gap between Desktop and the Cloud for Escience Applications. In: Proceedings IEEE 3rd International Conference on Cloud Computing, pp 474–481. IEEE (2010)Google Scholar
  3. 3.
    Vecchiola, C., Pandey, S., Buyya, R.: High-Performance Cloud Computing: a View of Scientific Applications. In: Proceedings 10th International Symposium on Pervasive Systems Algorithms, and Networks, pp 4–16. IEEE (2009)Google Scholar
  4. 4.
    Ramakrishnan, L., Jackson, K.R., Canon, S., Cholia, S., Shalf, J.: Defining Future Platform Requirements for e-Science Clouds. In: Proceedings 1st ACM Symposium on Cloud Computing, pp 101–106. ACM (2010)Google Scholar
  5. 5.
    Herbst, N.R., Kounev, S., Reussner, R.: Elasticity in Cloud Computing: What It Is, and What It is Not. In: Proceedings 10th International Conference on Autonomic Computing, pp 23–27. USENIX (2013)Google Scholar
  6. 6.
    Galante, G., Bona, L.C.E.: A Survey on Cloud Computing Elasticity. In: Proceedings IEEE/ACM 5th International Conference on Utility and Cloud Computing, pp 263–270. IEEE (2012)Google Scholar
  7. 7.
    Lorido-Botran, T., Miguel-Alonso, J., Lozano, J.: A review of auto-scaling techniques for elastic applications in cloud environments. J. Grid Comput. 12(4), 559–592 (2014)CrossRefGoogle Scholar
  8. 8.
    Chieu, T.C., Mohindra, A., Karve, A.A., Segal, A.: Dynamic Scaling of Web Applications in a Virtualized Cloud Computing Environment. In: Proceedings IEEE International Conference on e-Business Engineering, pp 281–286. IEEE (2009)Google Scholar
  9. 9.
    Armbrust, M., Fox, A., Griffith, R., Joseph, A.D., Katz, a., Konwinski, A., Lee, G., Patterson, D., Rabkin, A., Stoica, Ionaharia, M.: A view of cloud computing. Commun. ACM 53(4) (2010)Google Scholar
  10. 10.
    Wang, L., Zhan, J., Shi, W., Liang, Y.: Cloud, can scientific communities benefit from the economies of scale? IEEE Trans. Parallel Distrib. Syst. 23(2), 296–303 (2012)CrossRefGoogle Scholar
  11. 11.
    Oliveira, D., Ogasawara, E.: Is cloud computing the solution for brazilian researchers?. Intl. J. Comput. Appl. 6(8), 19–23 (2010)Google Scholar
  12. 12.
    Taifi, M., Shi, J. Y., Khreishah, A.: SpotMPI: a Framework for Auction-Based HPC Computing Using Amazon Spot Instances. Springer-Verlag (2011)Google Scholar
  13. 13.
    Chohan, N., Castillo, C., Spreitzer, M., Steinder, M., Tantawi, A., Krintz, C.: See Spot Run: Using Spot Instances for Mapreduce Workflows. In: Proceedings 2nd USENIX Conference on Hot Topics in Cloud Computing. USENIX (2010)Google Scholar
  14. 14.
    Vo, H.T., Chen, C., Ooi, B.C.: Towards elastic transactional cloud storage with range query support. Proc. VLDB Endowment 3(1-2), 506–514 (2010)CrossRefGoogle Scholar
  15. 15.
    Nicolae, B., Riteau, P., Keahey, K.: Bursting the Cloud Data Bubble: Towards Transparent Storage Elasticity in Iaas Clouds. In: 2014 IEEE 28th Intl Parallel and Distributed Processing Symposium, pp 135–144. IEEE (2014)Google Scholar
  16. 16.
    Iordache, A., Morin, C., Parlavantzas, N., Riteau, P.: Resilin: Elastic MapReduce over Multiple Clouds. In: Technical Report RR-8081, INRIA, Rennes, France (2012)Google Scholar
  17. 17.
    Lin, C., Lu, S.: SCPOR: an Elastic Workflow Scheduling Algorithm for Services Computing. In: Proceedings the 5th IEEE International Conference on Service-Oriented Computing and Applications, pp 1–8. SOCA, IEEE (2011)Google Scholar
  18. 18.
    Leslie, L., Sato, C., Lee, Y., Jiang, Q., Zomaya, A.: DEWE: a Framework for Distributed Elastic Scientific Workflow Execution. In: 13th Australasian Symp. on Parallel and Distributed Computing, pp 3–10. AusPDC, ACS (2015)Google Scholar
  19. 19.
    Oliveira, D., Baio, F.A., Mattoso, M.: Migrating Scientific Experiments to the Cloud. (2 july 2015, last accessed)
  20. 20.
    Truong, H., Dustdar, S.: Cloud computing for small research groups in computational science and engineering: Current status and outlook. Computing 91(1), 75–91 (2011)CrossRefMATHGoogle Scholar
  21. 21.
    Helix Nebula - The Science Cloud. (02 july 2015, last accessed)
  22. 22.
    Science Clouds. (02 july 2015, last accessed)
  23. 23.
    CloudLab. (02 july 2015, last accessed)
  24. 24.
    Amazon Web Services. (02 july 2015, last accessed)
  25. 25.
    Sabalcore. (20 july 2014, last accessed)
  26. 26.
    e-Science Central. (07 july 2015, last accessed)
  27. 27.
    Calheiros, R.N., Vecchiola, C., Karunamoorthy, D., Buyya, R.: The aneka platform and QoS-driven resource provisioning for elastic applications on hybrid clouds. Future Gener. Comput. Syst. 28(6), 861–870 (2011)CrossRefGoogle Scholar
  28. 28.
    Marshall, P., Keahey, K., Freeman, T.: Elastic Site: Using Clouds to Elastically Extend Site Resources. In: Proceedings 10th IEEE/ACM International Conference on Cluster Cloud and Grid Computing, pp 43–52. IEEE (2010)Google Scholar
  29. 29.
    Bicer, T., Chiu, D., Agrawal, G.: A Framework for Data-Intensive Computing with Cloud Bursting. In: Proceedings Conference on High Performance Computing Networking, Storage and Analysis Companion, pp 5–6. ACM (2011)Google Scholar
  30. 30.
    Calatrava, A., Moltó, G., Hernandez, V.: Combining Grid and Cloud Resources for Hybrid Scientific Computing Executions. In: Proceedings 3rd International Conference on Cloud Computing Technology and Science, pp 494–501. IEEE (2011)Google Scholar
  31. 31.
    Mateescu, G., Gentzsch, W., Ribbens, C. J.: Hybrid Computing-Where HPC meets grid and cloud computing. Future Gener. Comput. Syst. 27(5), 440–453 (2011)CrossRefGoogle Scholar
  32. 32.
    He, Q., Zhou, S., Kobler, B., Duffy, D., Mcglynn, T.: Case Study for Running HPC Applications in Public Clouds. In: Proceedings 19th ACM International Symposium on High Performance Distributed Computing, pp 395–401. ACM (2010)Google Scholar
  33. 33.
    Li, J., Humphrey, M., Cheah, Y., Ryu, Y., Agarwal, D., Jackson, K., Ingen, C.: Fault Tolerance and Scaling in E-Science Cloud Applications: Observations from the Continuing Development of MODISAzure. In: Proceedings 6Th International Conference on e-Science, pp 246–253. IEEE (2010)Google Scholar
  34. 34.
    Edlund, A., Koopmans, M., Shah, Z. A., Livenson, I., Orellana, F., Kommeri, J., Tuisku, M., Lehtovuori, P., Hansen, K. M., Neukirchen, H., Hvannberg, E.: Practical Cloud Evaluation from a Nordic Escience User Perspective. In: Proceedings 5th International Workshop on Virtualization Technologies in Distributed Computing, pp 29–38. ACM (2011)Google Scholar
  35. 35.
    Mauch, V., Kunze, M., Hillenbrand, M.: High performance cloud computing. Future Gener. Comput. Syst. 29(6), 1408–1416 (2013)CrossRefGoogle Scholar
  36. 36.
    Vöckler, J.S., Juve, G., Deelman, E., Rynge, M., Berriman, B.: Experiences Using Cloud Computing for a Scientific Workflow Application. In: Proceedings 2nd International Workshop on Scientific Cloud Computing, pp 15–24. ACM (2011)Google Scholar
  37. 37.
    Keller, M., Meister, D., Brinkmann, A., Terboven, C., Bischof, C.: eScience Cloud Infrastructure. In: Proceedings 37th Euromicro Conference on Software Engineering and Advanced Applications, pp 188–195. IEEE (2011)Google Scholar
  38. 38.
    CERN: CERNVM. (15 january 2015, last accessed)
  39. 39.
    Hellerstein, J.L., Kohlhoff, K.J., Konerding, D.E.: Science in the Cloud: Accelerating Discovery in the 21st Century. IEEE Internet Comput. 16(4), 64–68 (2012)CrossRefGoogle Scholar
  40. 40.
    Sakr, S., Liu, A., Batista, D.M., Alomari, M.: A survey of large scale data management approaches in cloud environments. IEEE Commun. Surv. Tutorials 13(3), 311–336 (2011)CrossRefGoogle Scholar
  41. 41.
    Jha, S., Katz, D. S., Luckow, A., Merzky, A., Stamou, K.: Understanding Scientific Applications for Cloud Environments. In: Buyya, R., Broberg, J., Goscinski, A.M. (eds.) Cloud Computing: Principles and Paradigms. Wiley (2011)Google Scholar
  42. 42.
    Moreno-Vozmediano, R., Montero, R.S., Llorente, I.M.: Elastic Management of Cluster-Based Services in the Cloud. In: Proceedings the 1st Workshop on Automated Control for Datacenters and Clouds, pp 19–24. ACDC, ACM (2009)Google Scholar
  43. 43.
    Nie, L., Xu, Z.: An Adaptive Scheduling Mechanism for Elastic Grid Computing. In: 5th International Conference on Semantics, Knowledge and Grid, pp 184–191. SKG (2009)Google Scholar
  44. 44.
    Bientinesi, P., Iakymchuk, R., Napper, J.: HPC On competitive cloud resources. In: Furht, b., Escalante, A. (eds.) Handbook of Cloud Computing. Springer (2010)Google Scholar
  45. 45.
    Evangelinos, C., Hill, C.N.: Cloud Computing for Parallel Scientific HPC Applications: Feasibility of Running Coupled Atmosphere-Ocean Climate Models on Amazon’s EC2. ACM (2008)Google Scholar
  46. 46.
    Rehr, J.J., Vila, F.D., Gardner, J.P., Svec, L., Prange, M.: Scientific computing in the cloud. Comput. Sci. Eng. 12(3), 34–43 (2010)CrossRefGoogle Scholar
  47. 47.
    Gupta, A., Milojicic, D.: Evaluation of HPC applications on cloud. Technical report HPL-2011-132, HP laboratories, palo alto USA (2011)CrossRefGoogle Scholar
  48. 48.
    Church, P., Goscinski, A.: Iaas Clouds Vs. Clusters for HPC: a Performance Study. In: Proceedings 2nd International Conference on Cloud Computing, GRIDs, and Virtualization, pp 39–45. IARIA (2011)Google Scholar
  49. 49.
    VMWare vSphere. (02 july 2015, last accessed)
  50. 50.
    Simons, J.E., Buell, J.: Virtualizing high performance computing. ACM Oper. Syst. Rev. 44 (4), 136–145 (2010)CrossRefGoogle Scholar
  51. 51.
    Schad, J., Dittrich, J., Quiané-Ruiz, J.: Runtime measurements in the cloud: observing, Analyzing, and Reducing Variance. Proc. Very Large Database Endowment 3(1-2), 460–471 (2010)Google Scholar
  52. 52.
    Phillips, S.C., Engen, V., Papay, J.: Snow White Clouds and the Seven Dwarfs. In: Proceedings 3rd IEEE International Conference on Cloud Computing Technology and Science, pp 738–745. IEEE (2011)Google Scholar
  53. 53.
    Rego, P.A.L., Coutinho, E.F., Gomes, D.G., Souza, J.N.: FairCPU: Architecture for Allocation of Virtual Machines Using Processing Features. In: Proceedings 4th International Conference on Utility and Cloud Computing, pp 371–376. IEEE (2011)Google Scholar
  54. 54.
    Nanath, K., Pillai, R.: A model for Cost-Benefit analysis of cloud computing. J. Intl. Technol. Inf. Manag. 22(3), 93–117 (2013)Google Scholar
  55. 55.
    Negru, C., Cristea, V.: Cost models - pillars for efficient cloud computing: Position paper. Intl. J. Intell. Syst. Technol. Appl. 12(1), 28–38 (2013)Google Scholar
  56. 56.
    Berriman, G.B., Juve, G., Vckler, J.S., Deelman, E., Rynge, M.: The application of cloud computing to scientific workflows: a study of cost and performance. Proc. Royal Soc. Assoc. 371(1983), 1–14 (2012)Google Scholar
  57. 57.
    Fox, G., Gannon, D.: Using Clouds for Technical Computing. In: Catlett, C., Gentzsch, W., Grandinetti, L., Joubert, G., Vazquez-Poletti, J. (eds.) Cloud Computing and Big Data, pp 81–102. IOS Press (2013)Google Scholar
  58. 58.
    Li, A., Yang, X., Kandula, S., Zhang, M.: CloudCmp: Comparing Public Cloud Providers. In: Proceedings 10th Annual Conference on Internet Measurement, pp 1–14. ACM (2010)Google Scholar
  59. 59.
    Xiaotao, Y., Aili, L., Lin, Z.: Research of High Performance Computing with Clouds. In: Proceedings 3rd International Symposium on Computer Science and Computational Technology, pp 289–293. Academy Publisher (2010)Google Scholar
  60. 60.
    Santos, N., Gummadi, K.P., Rodrigues, R.: Towards Trusted Cloud Computing. In: Proceedings Conference on Hot Topics in Cloud Computing. USENIX (2009)Google Scholar
  61. 61.
    Zissis, D., Lekkas, D.: Addressing cloud computing security issues. Future Gener. Comput. Syst. 28(3), 583–592 (2012)CrossRefGoogle Scholar
  62. 62.
    Chen, W., Deelman, E.: Integration of Workflow Partitioning and Resource Provisioning. In: Proceedings 12Th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, pp 764–768. IEEE (2012)Google Scholar
  63. 63.
    Thakar, A., Szalay, A., Church, K., Terzis, A.: Large Science Databases Are Cloud Services Ready for Them?. Sci. Program. 19(2-3), 147–159 (2011)Google Scholar
  64. 64.
    Yuan, D., Yang, Y., Liu, X., Chen, J.: A data placement strategy in scientific cloud workflows. Future Gener. Comput. Syst. 26(8), 1200–1214 (2010)CrossRefGoogle Scholar
  65. 65.
    Lin, J.W., Chen, C.H.: Interference-aware virtual machine placement in cloud computing systems. In: International Conference on Computer Information Science. Volume 2 of ICCIS, pp 598–603 (2012)Google Scholar
  66. 66.
    Gupta, A., Milojicic, D., Kalé, L.V.: Optimizing VM Placement for HPC in the Cloud. In: Proceedings the 2012 Workshop on Cloud Services, Federation, and the 8th Open Cirrus Summit, pp 1–6. FederatedClouds, ACM (2012)Google Scholar
  67. 67.
    Canali, C., Lancellotti, R.: Automatic Virtual Machine Clustering Based on Bhattacharyya Distance for Multi-cloud Systems. In: Proceedings the 2013 International Workshop on Multi-cloud Applications and Federated Clouds, pp 45–52. MultiCloud, ACM (2013)Google Scholar
  68. 68.
    Rackspace. (15 january 2015, last accessed)
  69. 69.
    GoGrid. (15 january 2015, last accessed)
  70. 70.
    Joyent. (02 july 2015, last accessed)
  71. 71.
    Profitbricks. (02 july 2015, last accessed)
  72. 72.
    CloudSigma. (02 july 2015, last accessed)
  73. 73.
    CloudSigma. (02 july 2015, last accessed)
  74. 74.
    RightScale. (15 january 2015, last accessed)
  75. 75.
    Caron, E., Desprez, F., Rodero-Merino, L., Muresan, A.: Auto-scaling, Load Balancing and Monitoring in Commercial and Open-Source Clouds. In: Wang, L., Ranjan, R., Chen, J., Benatallah, B. (eds.) Cloud Computing: Methodology, Systems, and Applications. Taylor and Francis Group (2011)Google Scholar
  76. 76.
    Google App Engine. (26 june 2015, last accessed)
  77. 77.
    Microsoft Azure. (15 january 2015, last accessed)
  78. 78.
    Vaquero, L.M., Rodero-Merino, L., Buyya, R.: Dynamically scaling applications in the cloud. Comput. Commun. Rev. 41(1), 45–52 (2011)CrossRefGoogle Scholar
  79. 79.
    Roy, N., Dubey, A., Gokhale, A.: Efficient Autoscaling in the Cloud Using Predictive Models forWorkload Forecasting. In: Proceedings IEEE 4th Interntional Conference on Cloud Computing, pp 500–507. IEEE (2011)Google Scholar
  80. 80.
    Raveendran, A., Bicer, T., Agrawal, G.: A Framework for Elastic Execution of Existing MPI Programs. In: Proceedings IEEE International Symposium on Parallel and Distributed Processing Workshops and PhD Forum, pp 940–947. IEEE (2011)Google Scholar
  81. 81.
    Das, S., Agrawal, D., El Abbadi, A.: ElasTraS: An Elastic Transactional Data Store in the Cloud. In: Proceedings Conference on Hot Topics in Cloud Computing, pp 1–5. USENIX (2009)Google Scholar
  82. 82.
    Agrawal, D., El Abbadi, A., Das, S., Elmore, A.J.: Database Scalability, Elasticity, and Autonomy in the Cloud. In: Proceedings the 16th International Conference on Database Systems for Advanced Applications, pp 2–15. DASFAA, Springer (2011)Google Scholar
  83. 83.
    Pokorny, J.: NoSQL Databases: A Step to Database Scalability in Web Environment. In: Proceedings 13th International Conference on Information Integration and Web-based Applications and Services, pp 278–283. ACM (2011)Google Scholar
  84. 84.
    Villegas, D., Rodero, I., Fong, L., Bobroff, N., Liu, Y., Parashar, M., Sadjadi, S.: The Role of Grid Computing Technologies in Cloud Computing. In: Furht, B., Escalante, A. (eds.) Handbook of Cloud Computing. Springer (2010)Google Scholar
  85. 85.
    Costa, R., Brasileiro, F., De Souza Filho, G. L., Sousa, D. M.: Just in Time Clouds: Enabling Highly-Elastic Public Clouds over Low Scale Amortized Resources. Technical Report TR-3, Federal University of Campina Grande, Campina Grande, Brazil (2010)Google Scholar
  86. 86.
    Petcu, D.: Consuming resources and services from multiple clouds. J. Grid Comput. 12(2), 321–345 (2014)CrossRefGoogle Scholar
  87. 87.
    Papazoglou, M.P., Vaquero, L.M.: Knowledge-Intensive Cloud Services: Transforming the Cloud Delivery Stack. In: Kantola, J., Karwowski, W. (eds.) Knowledge Service Engineering Handbook. CRC Press (2012)Google Scholar
  88. 88.
    Zhang, Z., Wu, C., Cheung, D.W.: A survey on cloud interoperability: taxonomies, Standards, and Practice. SIGMETRICS Perform. Eval. Rev. 40(4), 13–22 (2013)CrossRefGoogle Scholar
  89. 89.
    Islam, S., Lee, K., Fekete, A., Liu, A.: How A Consumer Can Measure Elasticity for Cloud Platforms. Technical Report 680, School of Information Technologies, University of Sydney, Sydney, Australia (2011)Google Scholar
  90. 90.
    Suleiman, B., Sakr, S., Jeffery, R., Liu, A.: On understanding the economics and elasticity challenges of deploying business applications on public cloud infrastructure. J. Internet Serv. Appl. 3(2), 173–193 (2012)CrossRefGoogle Scholar
  91. 91.
    Agmon Ben-Yehuda, O., Ben-Yehuda, M., Schuster, A., Tsafrir, D.: The Resource-as-a-service (RaaS) Cloud. In: Proceedings 4th USENIX Conference on Hot Topics in Cloud Computing. USENIX (2012)Google Scholar
  92. 92.
    Han, R., Guo, L., Ghanem, M.M., Guo, Y.: Lightweight Resource Scaling for Cloud Applications. In: Proceedings the 2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, pp 644–651. CCGRID, IEEE (2012)Google Scholar
  93. 93.
    Moltó, G., Caballer, M., Romero, E., De Alfonso, C.: Elastic Memory Management of Virtualized Infrastructures for Applications with Dynamic Memory Requirements. Proced. Comput. Sci. 18(0), 159–168 (2013)CrossRefGoogle Scholar
  94. 94.
    Galante, G., Bona, L.C.E.: Supporting Elasticity in OpenMP Applications. In: Proceedings 22th Euromicro Conference on Parallel, Distributed and Network-Based Processing. IEEE (2014)Google Scholar
  95. 95.
    Brebner, P.C.: Is Your Cloud Elastic Enough?: Performance Modelling the Elasticity of Infrastructure As a Service (IaaS) Cloud Applications. In: Proceedings 3rd ACM/SPEC International Conference on Performance Engineering, pp 263?-266. ACM (2012)Google Scholar
  96. 96.
    Mao, M., Humphrey, M.: A Performance Study on the VM Startup Time in the Cloud. In: Proceedings 5th IEEE International Conference on Cloud Computing, pp 423–430. IEEE (2012)Google Scholar
  97. 97.
    Righi, R., Rodrigues, V., Andre da Costa, C., Galante, G., Bona, L., Ferreto, T.: Autoelastic: automatic resource elasticity for high performance applications in the cloud. IEEE Trans. Cloud Comput. PP(99), 1–1 (2015)Google Scholar
  98. 98.
    Srirama, S.N., Jakovits, P., Vainikko, E.: Adapting scientific computing problems to clouds using MapReduce. Future Gener. Comput. Syst. 28(1), 184–192 (2012)CrossRefGoogle Scholar
  99. 99.
    Bunch, C., Drawert, B., Norman, M.: MapScale: A Cloud Environment for Scientific Computing. Technical report. University of California, Santa Barbara, USA (2009)Google Scholar
  100. 100.
    Dean, J., Ghemawat, S.: Mapreduce: simplified data processing on large clusters. Commun. ACM 51(1), 107–113 (2008)CrossRefGoogle Scholar
  101. 101.
    Pandey, S., Karunamoorthy, D., Buyya, R.: Workflow Engine for Clouds. In: Buyya, R., Broberg, J., Goscinski, A.M. (eds.) Cloud Computing: Principles and Paradigms. Wiley (2011)Google Scholar
  102. 102.
    Byun, E. K., Kee, Y. S., Kim, J. S., Maeng, S.: Cost optimized provisioning of elastic resources for application workflows. Future Gener. Comput. Syst. 27(8), 1011–1026 (2011)CrossRefGoogle Scholar
  103. 103.
    Shams, K.S., Powell, M.W., Crockett, T.M., Norris, J.S., Rossi, R., Soderstrom, T.: Polyphony: A Workflow Orchestration Framework for Cloud Computing. In: Proceedings 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing, pp 606–611. IEEE (2010)Google Scholar
  104. 104.
    Kranjc, J., Podpečan, V., Lavrač, N.: ClowdFlows: A Cloud Based Scientific Workflow Platform. In: Proceedings European Conference on Machine Learning and Knowledge Discovery in Databases-Part II, pp 816–819. Springer (2012)Google Scholar
  105. 105.
    Rajan, D., Canino, A., Izaguirre, J.A., Thain, D.: Converting a High Performance Application to an Elastic Cloud Application. In: Proceedings 3rd IEEE International Conference on Cloud Computing Technology and Science, pp 383–390. IEEE (2011)Google Scholar
  106. 106.
    Galante, G., Bona, L.C.E.: Constructing Elastic Scientific Applications Using Elasticity Primitives. In: Murgante, B., Misra, S., Carlini, M., Torre, C.M., Nguyen, H.Q., Taniar, D., Apduhan, B.O., Gervasi, O. (eds.) Proceedings 13th International Conference on Computational Science and Its Applications Volume 5, Lecture Notes in Computer Science. Springer (2013)Google Scholar
  107. 107.
    Cruz, F., Maia, F., Matos, M., Oliveira, R., Paulo, J.a., Pereira, J., Vilaça, R.: MeT: Workload Aware Elasticity for NoSQL. In: Proceedings 8th ACM European Conference on Computer Systems, pp 183–196. ACM (2013)Google Scholar
  108. 108.
    Konstantinou, I., Angelou, E., Boumpouka, C., Tsoumakos, D., Koziris, N.: On the Elasticity of NoSQL Databases over Cloud Management Platforms. In: Proceedings 20th ACM International Conference on Information and Knowledge Management, pp 2385–2388. ACM (2011)Google Scholar
  109. 109.
    Minhas, U.F.: Scalable and Highly Available Database Systems in the Cloud. PhD thesis, University of Waterloo, Ontario, Canada (2013)Google Scholar
  110. 110.
    ScaleBase. (02 july 2015, last accessed)
  111. 111.
    DMTF: Open Virtualization Format. (02 july 2015, last accessed)
  112. 112.
    SNIA: Cloud Data Management Interface. (02 july 2015, last accessed)
  113. 113.
    OGF: Open Cloud Computing Interface. (02 july 2015, last accessed)
  114. 114.
    Buyya, R., Ranjan, R., Calheiros, R.N.: Inter-Cloud: Utility-oriented Federation of Cloud Computing Environments for Scaling of Application Services. In: Proceedings 10th International Conference on Algorithms and Architectures for Parallel Processing, pp 13–31. Springer (2010)Google Scholar
  115. 115.
    Villegas, D., Bobroff, N., Rodero, I., Delgado, J., Liu, Y., Devarakonda, A., Fong, L., Masoud Sadjadi, S., Parashar, M.: Cloud federation in a layered service model. J. Comput. Syst. Sci. 78(5), 1330–1344 (2012)CrossRefGoogle Scholar
  116. 116.
    Yangui, S., Marshall, I.J., Laisne, J.P., Tata, S.: Compatibleone: The open source cloud broker. J. Grid Comput. 12(1), 93–109 (2014)CrossRefGoogle Scholar
  117. 117.
    EU Brazil Cloud Connect . (28 june 2015, last accessed)
  118. 118.
    European Grid Infrastructure. (28 june 2015, last accessed)
  119. 119.
    Zhu, J., Jiang, Z., Xiao, Z.: Twinkle: A Fast Resource Provisioning Mechanism for Internet Services. In: Proceedings 30th IEEE International Conference on Computer Communications, pp 802–810. IEEE (2011)Google Scholar
  120. 120.
    Tang, C.: A High-Performance Virtual Machine Image Format for Cloud. In: Proceedings USENIX Technical Conference. USENIX (2011)Google Scholar
  121. 121.
    De, P., Gupta, M., Soni, M., Thatte, A.: Caching VM Instances for Fast VM Provisioning: A Comparative Evaluation. In: Proceedings 18th International Conference on Parallel Processing, pp 325–336. Springer (2012)Google Scholar
  122. 122.
    Google Compute Engine. (26 june 2015, last accessed)
  123. 123.
    Yu, L., Thain, D.: Resource Management for Elastic Cloud Workflows. In: Proceedings the 2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, pp 775–780. CCGRID, IEEE (2012)Google Scholar
  124. 124.
    Wottrich, R., Azevedo, R., Araujo, G.: Cloudbased OpenMP Parallelization Using a MapReduce Runtime. In: 26th IEEE International Symposium on Computer Architecture and High Performance Computing, pp 334–341. SBAC-PAD, IEEE (2014)Google Scholar
  125. 125.
    Caballer, M., De Alfonso, C., Molt, G., Romero, E., Blanquer, I., Garca, A.: CodeCloud: A platform to enable execution of programming models on the Clouds. J. Syst. Softw. 93(0), 187–198 (2014)CrossRefGoogle Scholar
  126. 126.
    Vaquero, L.M., Rodero-Merino, L., Caceres, J., Lindner, M.: A break in the clouds: Towards a cloud definition. SIGCOMM Comput. Commun. Rev. 39(1), 50–55 (2008)CrossRefGoogle Scholar
  127. 127.
    Goiri, I., Guitart, J., Torres, J.: Characterizing Cloud Federation for Enhancing Providers’ Profit. In: Proceedings the 2010 IEEE 3rd International Confernce on Cloud Computing. CLOUD, IEEE (2010)Google Scholar
  128. 128.
    Xavier, M.G., Neves, M.V., Rossi, F.D., Ferreto, T.C., Lange, T., De Rose, C.A.F.: Performance Evaluation of Container-Based Virtualization for High Performance Computing Environments. In: Proceedings the 2013 21st Euromicro International Conference on Parallel, Distributed, and Network-Based Processing, pp 233–240. PDP, IEEE (2013)Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2016

Authors and Affiliations

  • Guilherme Galante
    • 1
  • Luis Carlos Erpen De Bona
    • 2
  • Antonio Roberto Mury
    • 3
  • Bruno Schulze
    • 3
  • Rodrigo da Rosa Righi
    • 4
  1. 1.Department of Computer ScienceWestern Paraná State UniversityCascavelBrazil
  2. 2.Department of InformaticsFederal University of ParanáCuritibaBrazil
  3. 3.National Laboratory for Scientific ComputingPetrópolisBrazil
  4. 4.Applied Computing Graduate Program, UnisinosSão LeopoldoBrazil

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