Elastipipe: On Providing Cloud Elasticity for Pipeline-structured Applications

  • Rodrigo da Rosa RighiEmail author
  • Mateus Aubin
  • Cristiano André da Costa
  • Antonio Marcos Alberti
  • Arismar Cerqueira Sodre
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 1)


Although offering clear benefits for Web and business-critical demands, the use of cloud elasticity still imposes challenges when trying to reap its benefits over complex applications such as those modeled as pipelines. This often happens because replication, the standard technique for resource reorganization, by default, doesn’t address both function-level parallelism and communication between working VMs. Taking into account this background, here we are proposing a model named Elastipipe to provide automatic elasticity over pipelines-structured applications. Our main goal is to reduce total execution time for a set of tasks in a way that is effortless to cloud users, eliminating the need for any preconfiguration. Elastipipe’s contribution consists in a framework designed to provide a new concept named flexible superscalar pipelines, in which scaling operations and load balancing take place over different elasticity units. An elasticity unit refers to a set of one or more stages of a pipeline that will be grouped together under the same elasticity rules. Using a real workload from an IT Brazilian company, the Elastipipe prototype presented performance gains of up to 60% when confronted with a non-elastic approach. In addition, we demonstrated that the functional decomposition of pipeline stages (CPU-bound, memory-bound, and so on) in corresponding elasticity units was responsible for the best results in terms of performance and cost.


Cloud Computing Virtual Machine Cloud Provider Total Execution Time Functional Decomposition 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    J. Baliga, R.W.A. Ayre, K. Hinton, and R.S. Tucker. Green cloud computing: Balancing energy in processing, storage, and transport. Proceedings of the IEEE, 99(1):149–167, 2011.Google Scholar
  2. 2.
    S. Dustdar, Yike Guo, Rui Han, B. Satzger, and Hong-Linh Truong. Programming directives for elastic computing. Internet Comp, IEEE, 16(6):72–77, Nov 2012.Google Scholar
  3. 3.
    Soodeh Farokhi, Pooyan Jamshidi, Ivona Brandic, and Erik Elmroth. Selfadaptation challenges for cloud-based applications : A control theoretic perspective. In 10th Int. Workshop on Feedback Computing. ACM, 2015.Google Scholar
  4. 4.
    Guilherme Galante, Luis De Bona, Antonio Mury, Bruno Schulze, and Rodrigo Righi. An analysis of public clouds elasticity in the execution of scientific applications: a survey. J. Grid Comput., 14(2):193–216, 2016.Google Scholar
  5. 5.
    Nikolas Roman Herbst, Samuel Kounev, Andreas Weber, and Henning Groenda. Bungee: An elasticity benchmark for self-adaptive iaas cloud environments. In Proc. of the 10th Int. Symp. on Software Engineering for Adaptive and Self-Managing Systems, SEAMS ’15, pages 46–56, Piscataway, NJ, USA, 2015. IEEE Press.Google Scholar
  6. 6.
    Shigeru Imai, Thomas Chestna, and Carlos A. Varela. Elastic Scalable Cloud Computing Using Application-Level Migration. In 2012 IEEE Fifth Int. Conf. on Utility and Cloud Computing, pages 91–98, Honolulu, nov 2012. IEEE.Google Scholar
  7. 7.
    Janmartin Jahn, Santiago Pagani, Sebastian Kobbe, Jian-Jia Chen, and Jörg Henkel. Optimizations for configuring and mapping software pipelines in many core systems. In Proc. Design Automation Conference, DAC ’13, pages 130:1–130:8, New York, NY, USA, 2013. ACM.Google Scholar
  8. 8.
    Joao Loff and Joao Garcia. Vadara: Predictive Elasticity for Cloud Applications. In 2014 Int. Conf. on Cloud Computing Technology and Science, pages 541–546, Singapore, dec 2014. IEEE.Google Scholar
  9. 9.
    Paul Marshall, Kate Keahey, and Tim Freeman. Elastic Site: Using Clouds to Elastically Extend Site Resources. In 2010 10th IEEE/ACM Int. Conf. on Cluster, Cloud and Grid Computing, pages 43–52, Melbourne, 2010. IEEE.Google Scholar
  10. 10.
    Patrick Martin, Andrew Brown, Wendy Powley, and Jose Luis Vazquez-Poletti. Autonomic management of elastic services in the cloud. In 2011 IEEE Symp. on Computers and Communications (ISCC), pages 135–140, Kerkyra, jun 2011. IEEE.Google Scholar
  11. 11.
    Dinesh Rajan, Anthony Canino, Jesus A. Izaguirre, and Douglas Thain. Converting a high performance application to an elastic cloud application. In Proc. of Int. Conf. on Cloud Computing Technology and Science, CLOUDCOM ’11, pages 383–390, Washington, DC, USA, 2011. IEEE Computer Society.Google Scholar
  12. 12.
    A. Raveendran, T. Bicer, and G. Agrawal. A framework for elastic execution of existing mpi programs. In Parallel and Distributed Processing Workshops and Phd Forum (IPDPSW), 2011 IEEE International Symposium on, pages 940–947, May 2011.Google Scholar
  13. 13.
    R. Righi, V. Rodrigues, C. Andre daCosta, G. Galante, L. Bona, and T. Ferreto. Autoelastic: Automatic resource elasticity for high performance applications in the cloud. Cloud Computing, IEEE Transactions on, PP(99):1–1, 2015.Google Scholar
  14. 14.
    Nam-Luc Tran, Sabri Skhiri, and Esteban Zimányi. EQS: An Elastic and Scalable Message Queue for the Cloud. In 2011 IEEE Third Int. Conf. on Cloud Computing Technology and Science, pages 391–398, Athens, nov 2011. IEEE.Google Scholar
  15. 15.
    Xinwen Zhang, Sangoh Jeong, Anugeetha Kunjithapatham, and Simon Gibbs. Towards an Elastic Application Model for Augmenting Computing Capabilities of Mobile Platforms. In Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecomm. Eng., volume 48, pages 161–174. Springer, 2010.Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Rodrigo da Rosa Righi
    • 1
    Email author
  • Mateus Aubin
    • 1
  • Cristiano André da Costa
    • 1
  • Antonio Marcos Alberti
    • 2
  • Arismar Cerqueira Sodre
    • 2
  1. 1.Applied Computing Graduate Program – UnisinosLeopoldoBrazil
  2. 2.Instituto Nacional de Telecomunicações – INATELSanta Rita do SapucaíBrazil

Personalised recommendations