Skip to main content
SpringerLink
Log in

A Review of Auto-scaling Techniques for Elastic Applications in Cloud Environments

  • Published:
Journal of Grid Computing Aims and scope Submit manuscript

Abstract

Cloud computing environments allow customers to dynamically scale their applications. The key problem is how to lease the right amount of resources, on a pay-as-you-go basis. Application re-dimensioning can be implemented effortlessly, adapting the resources assigned to the application to the incoming user demand. However, the identification of the right amount of resources to lease in order to meet the required Service Level Agreement, while keeping the overall cost low, is not an easy task. Many techniques have been proposed for automating application scaling. We propose a classification of these techniques into five main categories: static threshold-based rules, control theory, reinforcement learning, queuing theory and time series analysis. Then we use this classification to carry out a literature review of proposals for auto-scaling in the cloud.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. RUBiS: Rice University Bidding System. http://rubis.ow2.org/. Online: Accessed 13 Sept 2012 (2009)

  2. Workload Patterns for Cloud Computing. http://watdenkt.veenhof.nu/2010/07/13/workload-patterns-for-cloud-computing/. Online: Accessed 29 Jan 2014 (2010)

  3. Amazon Elastic Compute Cloud (Amazon EC2). http://aws.amazon.com/ec2/. Online: Accessed 13 Sept 2012 (2012)

  4. Apache JMeter., http://jmeter.apache.org/. Online: Accessed 18 Sept 2012 (2012)

  5. AWS Elastic Beanstalk (beta). Easy to begin, Impossible to outgrow. http://aws.amazon.com/elasticbeanstalk/. Online: Accessed 13 Sept 2012 (2012)

  6. ClarkNet HTTP Trace (From the Internet Traffic Archive). http://ita.ee.lbl.gov/html/contrib/ClarkNet-HTTP.html. Online: Accessed 13 Sept 2012 (2012)

  7. CloudSim: A Framework for Modeling and Simulation of Cloud Computing Infrastructures and Services. http://www.cloudbus.org/cloudsim/. Online: Accessed 18 Sept 2012 (2012)

  8. CloudStone Project by Rad Lab Group., http://radlab.cs.berkeley.edu/wiki/Projects/Cloudstone/. Online: Accessed 13 Sept 2012 (2012)

  9. Eucalyptus Cloud., http://www.eucalyptus.com/. Online: Accessed 18 Sept 2012 (2012)

  10. Google App. Engine. http://cloud.google.com/products/. Online: Accessed 13 Sept 2012 (2012)

  11. Google Apps for Business. http://www.google.com/intl/es/enterprise/apps/business/products.html. Online: Accessed 13 Sept 2012 (2012)

  12. Google Cluster Data. Traces of Google workloads. http://code.google.com/p/googleclusterdata/. Online: Accessed 13 Sept 2012 (2012)

  13. Google Compute Engine. http://cloud.google.com/products/compute-engine.html/. Online: Accessed 13 Sept 2012 (2012)

  14. Greencloud - The green cloud simulator. http://greencloud.gforge.uni.lu/. Online: Accessed 18 Sept 2012 (2012)

  15. Kernel Based Virtual Machine. http://www.linux-kvm.org/. Online: Accessed 18 Sept 2012 (2012)

  16. MediaWiki. http://www.mediawiki.org/wiki/MediaWiki. Online: Accessed 24 Nov 2012 (2012)

  17. Microsoft Office 365. http://www.microsoft.com/en-us/office365/online-software.aspx. Online: Accessed 13 Sept 2012 (2012)

  18. Microsoft Windows Azure. https://www.windowsazure.com/en-us/. Online: Accessed 13 Sept 2012 (2012)

  19. OpenStack Cloud Software. Open source software for building private and public clouds. http://www.openstack.org/. Online: Accessed 18 Sept 2012 (2012)

  20. Rackspace. The open cloud company. http://www.rackspace.com/. Online: Accessed 13 Sept 2012 (2012)

  21. Rain Workload Toolkit. https://github.com/yungsters/rain-workload-toolkit/wiki. Online: Accessed 13 Sept 2012 (2012)

  22. RightScale Cloud Management. http://www.rightscale.com/. Online: Accessed 13 Sept 2012 (2012)

  23. RightScale. Set up Autoscaling using Voting Tags. http://support.rightscale.com/03-Tutorials/02-AWS/02-Website_Edition/Set_up_Autoscaling_using_Voting_Tags. Online: Accessed 13 Sept 2012 (2012)

  24. RUBBoS: Bulletin Board Benchmark. http://jmob.ow2.org/rubbos.html/. Online: Accessed 18 Sept 2012 (2012)

  25. Salesforce.com. http://www.salesforce.com/. Online: Accessed 13 Sept 2012 (2012)

  26. SPEC forms cloud benchmarking group. http://www.spec.org/osgcloud/press/cloudannouncement20120613.html. Online: Accessed 18 Sept 2012 (2012)

  27. The httperf HTTP load generator. http://code.google.com/p/httperf/. Online: Accessed 18 Sept 2012 (2012)

  28. TPC-C., http://www.tpc.org/tpcc/default.asp/. Online: Accessed 18 Sept 2012 (2012)

  29. TPC. Transaction Processing Performance Council. http://www.tpc.org/default.asp. Online: Accessed 13 Sept 2012 (2012)

  30. TPC-W. http://www.tpc.org/tpcw/default.asp. Online: Accessed 13 Sept 2012 (2012)

  31. VMware vCloud Director. Deliver Complete Virtual Datacenters for Consumption in Minutes. http://www.eucalyptus.com/. Online: Accessed 18 Sept 2012 (2012)

  32. VMware vSphere ESX and ESXi Info Center. http://www.vmware.com/es/products/datacenter-virtualization/vsphere/esxi-and-esx/overview.html. Online: Accessed 18 Sept 2012 (2012)

  33. WikiBench: A Web hosting benchmark. http://www.wikibench.eu. Online: Accessed 24 Nov 2012 (2012)

  34. Wikipedia access traces. http://www.wikibench.eu/?page_id=60. Online: Accessed 24 Nov 2012 (2012)

  35. World Cup 98 Trace (From the Internet Traffic Archive). http://ita.ee.lbl.gov/html/contrib/WorldCup.html. Online: Accessed 13 Sept 2012 (2012)

  36. Xen hypervisor. http://www.xen.org/. Online: Accessed 18 Sept 2012 (2012)

  37. Albus, J.: A new approach to manipulator control: The cerebellar model articulation controller (CMAC). Transaction of the ASME, Journal of Dynamic Systems, Measurement and Control (1975)

  38. Alhamazani, K., Ranjan, R., Mitra, K., Rabhi, F., Khan, S.U., Guabtni, A., Bhatnagar, V.: An Overview of the Commercial Cloud Monitoring Tools: Research Dimensions, Design Issues, and State-of-the-Art. arXiv:13126170 (2013)

  39. Ali-Eldin, A., Kihl, M., Tordsson, J., Elmroth, E.: Efficient provisioning of bursty scientific workloads on the cloud using adaptive elasticity control. In: Proceedings of the 3rd workshop on Scientific Cloud Computing Date - ScienceCloud ’12, p. 31–40. ACM (2012)

  40. Ali-Eldin, A., Tordsson, J., Elmroth, E.: An adaptive hybrid elasticity controller for cloud infrastructures. In: Network Operations and Management Symposium (NOMS), 2012, IEEE, IEEE, pp. 204–212 (2012)

  41. Bacigalupo, D.A., van Hemert, J., Usmani, A., Dillenberger, D.N., Wills, GB, Jarvis S.A.: Resource management of enterprise cloud systems using layered queuing and historical performance models. In: 2010 IEEE International Symposium on Parallel & Distributed Processing, Workshops and Phd Forum, (IPDPSW), IEEE, pp. 1–8 (2010). doi:10.1109/IPDPSW.2010.5470782

  42. Barrett, E., Howley, E., Duggan, J.: Applying reinforcement learning towards automating resource allocation and application scalability in the cloud. Concurrency and Computation: Practice and Experience (2012)

  43. Bodík, P., Griffith, R., Sutton, C., Fox, A., Jordan, M., Patterson, D.: Statistical machine learning makes automatic control practical for internet datacenters. HotCloud’09 Proceedings of the 2009 Conference on Hot Topics in Cloud Computing (2009)

  44. Brown, R., Meyer, R.: The fundamental theorem of exponential smoothing. Operations Research (1961)

  45. Bu, X., Rao, J., Xu, C.Z.: Coordinated Self-configuration of Virtual Machines and Appliances using A Model-free Learning Approach. IEEE Transactions on Parallel and Distributed Systems (2012)

  46. Caron, E., Desprez, F., Muresan, A.: Forecasting for Cloud computing on-demand resources based on pattern matching. Research Report RR-7217, INRIA (2010)

  47. Caron, E., Desprez, F., Muresan, A.: Pattern Matching Based Forecast of Non-periodic Repetitive Behavior for Cloud Clients. J. Grid Comput. 9(1), 49–64 (2011). doi:10.1007/s10723-010-9178-4

    Article  Google Scholar 

  48. Casalicchio, E., Silvestri, L.: Autonomic Management of Cloud-Based Systems: The Service Provider Perspective. In: Gelenbe, E., Lent, R. (eds.) Computer and Information Sciences III, pp. 39–47. Springer, London (2013). doi:10.1007/978-1-4471-4594-3_5

    Chapter  Google Scholar 

  49. Chandra, A., Gong, W., Shenoy, P.: Dynamic resource allocation for shared data centers using online measurements. In: Proceedings of the 11th International Conference on Quality of Service, pp. 381–398 (2003)

  50. Chen, G., He, W., Liu, J., Nath, S., Rigas, L., Xiao, L., Zhao, F.: Energy-aware server provisioning and load dispatching for connection-intensive internet services. In: Proceedings of the 5th USENIX Symposium on Networked Systems Design and Implementation, USENIX Association, vol. 8, pp. 337–350 (2008)

  51. Chieu, T.C., Mohindra, A., Karve, A.A., Segal, A.: Dynamic scaling of web applications in a virtualized cloud computing environment. In: IEEE International Conference on e-Business Engineering, ICEBE09, IEEE, pp. 281–286 (2009)

  52. Chieu, T.C., Mohindra, A., Karve, A.A.: Scalability and performance of web applications in a compute cloud. In: 2011 IEEE 8th International Conference on e-Business Engineering (ICEBE), pp. 317–323 (2011)

  53. Cormen, T.H., Stein, C., Rivest, R.L., Leiserson, C.E.: Introduction to algorithms, chapter 32: string matching. McGraw-Hill Higher Education (2001)

  54. Dutreilh, X., Moreau, A., Malenfant, J., Rivierre, N., Truck, I.: From data center resource allocation to control theory and back. In: Cloud Computing (CLOUD), 2010 IEEE 3rd International Conference on, IEEE pp. 410–417 (2010)

  55. Dutreilh, X., Kirgizov, S., Melekhova, O., Malenfant, J., Rivierre, N., Truck, I.: Using reinforcement learning for autonomic resource allocation in clouds: Towards a fully automated workflow. In: Seventh International Conference on Autonomic and Autonomous Systems, ICAS 2011, IEEE, pp. 67–74 (2011)

  56. Dutta, S., Gera, S., Verma, A., Viswanathan, B.: SmartScale: Automatic application scaling in enterprise clouds. In: 2012 IEEE Fifth International Conference on Cloud Computing, IEEE, pp. 221–228 (2012). doi:10.1109/CLOUD.2012.12

  57. Fang, W., Lu, Z., Wu, J., Cao, Z.: RPPS: a novel resource prediction and provisioning scheme in cloud data center. In: 2012 IEEE Ninth International Conference on Services Computing, IEEE, pp. 609–616 (2012). doi:10.1109/SCC.2012.47

  58. Gambi, A., Toffetti, G.: Modeling cloud performance with Kriging. In: 2012 34th International Conference on Software Engineering, (ICSE), IEEE, pp. 1439–1440 (2012). doi:10.1109/ICSE.2012.6227075

  59. Ghanbari, H., Simmons, B., Litoiu, M., Iszlai, G.: Exploring alternative approaches to implement an elasticity policy. In: 2011 IEEE International Conference on Cloud Computing (CLOUD), pp. 716–723 (2011)

  60. Gong, Z., Gu, X., Wilkes, J.: Press: predictive elastic resource scaling for cloud systems. In: 2010 International Conference on Network and Service Management (CNSM), pp. 9–16 (2010)

  61. Guitart, J., Torres, J., Ayguadé, E.: A survey on performance management for internet applications. Concurrency and Computation: Practice and Experience 22(1), 68–106 (2010). doi:10.1002/cpe.1470

    Article  Google Scholar 

  62. Han, R., Ghanem, M.M., Guo, L., Guo, Y., Osmond, M.: Enabling cost-aware and adaptive elasticity of multi-tier cloud applications. Futur. Gener. Comput. Syst. 32, 82–98 (2012)

  63. Han, R., Guo, L., Ghanem, M., Han, R., Guo, L., Ghanem, M.M., Guo, Y.: Lightweight Resource Scaling for Cloud Applications. Cluster, Cloud and Grid Computing (CCGrid), 2012 12th IEEE/ACM International Symposium on (2012)

  64. Hasan, M.Z., Magana, E., Clemm, A., Tucker, L., Gudreddi, S.L.D.: Integrated and autonomic cloud resource scaling. In: Network Operations and Management Symposium (NOMS), 2012 IEEE, IEEE, pp. 1327–1334 (2012)

  65. Huang, J., Li, C., Yu, J.: Resource prediction based on double exponential smoothing in cloud computing. In: 2012 2nd International Conference on Consumer Electronics, Communications and Networks (CECNet), pp. 2056–2060 (2012)

  66. Iqbal, W., Dailey, M.N., Carrera, D., Janecek, P.: Adaptive resource provisioning for read intensive multi-tier applications in the cloud. Futur. Gener. Comput. Syst. 27(6), 871–879 (2011). doi:10.1016/j.future.2010.10.016

    Article  Google Scholar 

  67. Islam, S., Keung, J., Lee, K., Liu, A.: Empirical prediction models for adaptive resource provisioning in the cloud. Futur. Gener. Comput. Syst. 28(1), 155–162 (2012). doi:10.1016/j.future.2011.05.027

    Article  Google Scholar 

  68. Jacob, B., Lanyon-Hogg, R., Nadgir, D.K., Yassin, A.F.: A practical guide to the IBM autonomic computing toolkit (2004)

  69. Kalyvianaki, E., Charalambous, T., Hand, S.: Self-adaptive and self-configured cpu resource provisioning for virtualized servers using kalman filters. In: Proceedings of the 6th International Conference on Autonomic Computing, ACM, pp. 117–126 (2009)

  70. Kertesz, A., Kecskemeti, G., Oriol, M., Kotcauer, P., Acs, S., Rodríguez, M., Mercè, O., Marosi, A.C., Marco, J., Franch, X.: Enhancing Federated Cloud Management with an Integrated Service Monitoring Approach. J. Grid Comput. (2013)

  71. Khatua, S., Ghosh, A., Mukherjee, N.: Optimizing the utilization of virtual resources in cloud environment. In: 2010 IEEE International Conference on Virtual Environments, Human-Computer Interfaces and Measurement Systems, IEEE, pp. 82–87 (2010). doi:10.1109/VECIMS.2010.5609349

  72. Koperek, P., Funika, W.: Dynamic business metrics-driven resource provisioning in cloud environments. In: Wyrzykowski, R., Dongarra, J., Karczewski, K., Waśniewski, J. (eds.) Parallel Processing and Applied Mathematics, Lecture Notes in Computer Science, vol. 7204, pp. 171–180. Springer, Berlin Heidelberg (2012). doi:10.1007/978-3-642-31500-8_18

    Google Scholar 

  73. Kupferman J, Silverman J, Jara P, Browne J. Scaling into the cloud. Tech. rep., University of California, Santa Barbara; CS270 - Advanced Operating Systems (2009). http://cs.ucsb.edu/jkupferman/docs/ScalingIntoTheClouds.pdf

  74. Lama, P., Zhou, X.: Autonomic Provisioning with Self-Adaptive Neural Fuzzy Control for End-to-end Delay Guarantee. In: 2010 IEEE International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems, IEEE, pp. 151–160 (2010). doi:10.1109/MASCOTS.2010.24

  75. Lim, H.C., Babu, S., Chase, J.S., Parekh, S.S.: Automated control in cloud computing: challenges and opportunities. In: Proceedings of the 1st Workshop on Automated Control for Datacenters and Clouds, ACM, New York, NY, USA, ACDC ’09, pp. 13–18 (2009). doi:10.1145/1555271.1555275

  76. Lim, H.C., Babu, S., Chase, J.S.: Automated control for elastic storage. In: Proceeding of the 7th International Conference on Autonomic Computing - ICAC ’10, p. 1. ACM Press, New York (2010)

    Google Scholar 

  77. Lorido-Botran, T., Miguel-Alonso, J., Lozano, J.A.: Comparison of Auto-scaling Techniques for Cloud Environments. In: Alberto, A., Del Barrio, G.B. (eds.) Actas de las XXIV Jornadas de Paralelismo, Servicio de Publicaciones (2013)

  78. Manvi, S.S., Shyam, G.K.: Resource management for Infrastructure as a Service (IaaS) in cloud computing: A survey. J. Netw. Comput. Appl. 41 424–440 (2014)

  79. Mao, M., Humphrey, M.: Auto-scaling to minimize cost and meet application deadlines in cloud workflows. In: Proceedings of 2011 International Conference for High Performance Computing, Networking, Storage and Analysis on - SC ’11, p. 1. ACM Press, New York (2011)

    Google Scholar 

  80. Mao, M., Humphrey, M.: A performance study on the VM startup time in the cloud. In: Proceedings of the 2012 IEEE Fifth International Conference on Cloud Computing, IEEE Computer Society, Washington, DC, USA, CLOUD ’12, pp. 423–430 (2012). doi:10.1109/CLOUD.2012.103

  81. Maurer, M., Brandic, I., Sakellariou, R.: Enacting slas in clouds using rules. Euro-Par. Parallel Processing (2011)

  82. Maurer, M., Breskovic, I., Emeakaroha, V.C., Brandic, I.: Revealing the MAPE loop for the autonomic management of Cloud infrastructures. In: 2011 IEEE Symposium on Computers and Communications (ISCC), pp. 147–152 (2011). doi:10.1109/ISCC.2011.5984008

  83. Menasce, D.A., Dowdy, L.W., Almeida, V.A.F.: Performance by Design: Computer Capacity Planning By Example. Prentice Hall, Upper Saddle River (2004)

    Google Scholar 

  84. Méndez Muñoz, V., Casajús Ramo, A., Fernández Albor, V., Graciani Diaz, R., Merino Arévalo, G.: Rafhyc: an architecture for constructing resilient services on federated hybrid clouds. J. Grid Comput. 11(4), 753–770 (2013). doi:10.1007/s10723-013-9279-y

    Article  Google Scholar 

  85. Mi, H., Wang, H., Yin, G., Zhou, Y., Shi, D., Yuan, L.: Online self-reconfiguration with performance guarantee for energy-efficient large-scale cloud computing data centers. In: 2010 IEEE International Conference on Services Computing (SCC), pp. 514–521 (2010)

  86. Moore, L.R., Bean, K., Ellahi, T.: Transforming reactive auto-scaling into proactive auto-scaling. In: Proceedings of the 3rd International Workshop on Cloud Data and Platforms, ACM, New York, NY, USA, CloudDP ’13, pp. 7–12 (2013). doi:10.1145/2460756.2460758

  87. Ostermann, S., Plankensteiner, K., Prodan, R., Fahringer, T.: GroudSim: An event-based simulation framework for computational grids and clouds. In: Guarracino, M., Vivien, F., Träff, J., Cannatoro, M., Danelutto, M., Hast, A., Perla, F., Knüpfer, A., Martino, B., Alexander, M. (eds.) Euro-Par 2010 Parallel Processing Workshops, Lecture Notes in Computer Science, vol. 6586, pp. 305–313. Springer, Berlin Heidelberg (2011). doi:10.1007/978-3-642-21878-1_38

  88. Padala, P., Hou, K.Y., Shin, K.G., Zhu, X., Uysal, M., Wang, Z., Singhal, S., Merchant, A.: Automated control of multiple virtualized resources. In: Proceedings of the 4th ACM European Conference on Computer Systems, ACM, pp. 13–26 (2009)

  89. Park, S.M., Humphrey, M.: Self-tuning virtual machines for predictable eScience. In: 2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid, IEEE, pp. 356–363 (2009). doi:10.1109/CCGRID.2009.84

  90. Patikirikorala, T., Colman, A.: Feedback controllers in the cloud. APSEC 2010, Cloud Workshop (2010)

  91. Prodan, R., Nae, V.: Prediction-based real-time resource provisioning for massively multiplayer online games. Futur. Gener. Comput. Syst. 25(7), 785–793 (2009). doi:10.1016/j.future.2008.11.002

    Article  Google Scholar 

  92. Rao, J., Bu, X., Xu, C.Z., Wang, L., Yin, G.: VCONF: a reinforcement learning approach to virtual machines auto-configuration. In: Proceedings of the 6th International Conference on Autonomic Computing, ACM, New York, NY, USA, ICAC ’09, pp. 137–146 (2009). doi:10.1145/1555228.1555263

  93. Rao, J., Bu, X., Xu, C.Z., Wang, K.: 8. In: 2011 IEEE 19th Annual International Symposium on Modelling, Analysis, and Simulation of Computer and Telecommunication Systems, IEEE, pp. 45–54 (2011). doi:10.1109/MASCOTS.2011.47

  94. Roy, N., Dubey, A., Gokhale, A.: Efficient Autoscaling in the Cloud Using Predictive Models for Workload Forecasting. In: 2011 IEEE 4th International Conference on Cloud Computing, IEEE, pp. 500–507 (2011). doi:10.1109/CLOUD.2011.42

  95. Shen, Z., Subbiah, S., Gu, X., Wilkes, J.: Cloudscale: Elastic resource scaling for multi-tenant cloud systems. Proceedings of the 2nd ACM Symposium on Cloud Computing (2011)

  96. Simmons, B., Ghanbari, H., Litoiu, M., Iszlai, G.: Managing a SaaS application in the cloud using PaaS policy sets and a strategy-tree. In: 2011 7th International Conference on Network and Service Management (CNSM), pp. 1–5 (2011)

  97. SPECweb2009, (2012) The httperf HTTP load generator. http://www.spec.org/web2009/. Online: Accessed 18 Sept 2012

  98. Sutton, R.S., Barto, A.G.: Introduction to Reinforcement Learning. Cambridge University Press (1998)

  99. Tesauro, G., Jong, N.K., Das, R., Bennani, M.N.: A Hybrid Reinforcement Learning Approach to Autonomic Resource Allocation. In: Proceedings of the 2006 IEEE International Conference on Autonomic Computing, IEEE Computer Society, Washington, DC, USA, ICAC ’06, pp. 65–73 (2006). doi:10.1109/ICAC.2006.1662383

  100. Urgaonkar, B., Shenoy, P., Chandra, A., Goyal, P., Wood, T.: Agile dynamic provisioning of multi-tier Internet applications. ACM Transactions on Autonomous and Adaptive Systems 3(1), 1–39 (2008). doi:10.1145/1342171.1342172

    Article  Google Scholar 

  101. Villela, D., Pradhan, P., Rubenstein, D.: Provisioning servers in the application tier for e-commerce systems. In: Twelfth IEEE International Workshop on Quality of Service, 2004. IWQOS 2004, pp. 57–66 (2004)

  102. Wang, L., Xu, J., Zhao, M., Fortes, J.: Adaptive virtual resource management with fuzzy model predictive control. In: Proceedings of the 8th ACM international conference on Autonomic computing - ICAC ’11, p. 191. ACM Press, New York (2011)

    Google Scholar 

  103. Wang, L., Xu, J., Zhao, M., Tu, Y., Fortes, J.A.B.: Fuzzy Modeling Based Resource Management for Virtualized Database Systems. In: 2011 IEEE 19th International Symposium on Modeling, Analysis & Simulation of Computer and Telecommunication Systems (MASCOTS), pp. 32–42 (2011)

  104. Watkins, C., Dayan, P.: Q-learning. Machine Learning (1992)

  105. Xu, C.Z., Rao, J., Bu, X.: URL: A unified reinforcement learning approach for autonomic cloud management. J. Parallel Distrib. Comput. 72(2), 95–105 (2012). doi:10.1016/j.jpdc.2011.10.003

    Article  Google Scholar 

  106. Xu, J., Zhao, M., Fortes, J., Carpenter, R., Yousif, M.: On the Use of Fuzzy Modeling in Virtualized Data Center Management. In: Proceedings of the Fourth International Conference on Autonomic Computing, IEEE Computer Society, Washington, DC, USA, ICAC ’07, p. 25 (2007). doi:10.1109/ICAC.2007.28

  107. Zhang, Q., Cherkasova, L., Smirni, E.: A regression-based analytic model for dynamic resource provisioning of multi-tier applications. In: Fourth International Conference on Autonomic Computing, 2007. ICAC’07, p. 27 (2007)

  108. Zhu, Q., Agrawal, G.: Resource Provisioning with Budget Constraints for Adaptive Applications in Cloud Environments. IEEE Trans. Serv. Comput. 5(4), 497–511 (2012). doi:10.1109/TSC.2011.61

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Intelligent Systems Group, University of the Basque Country, UPV/EHU, Paseo Manuel de Lardizabal, 1, 20018, Donostia-San Sebastián, Spain

    Tania Lorido-Botran, Jose Miguel-Alonso & Jose A. Lozano

  2. Department of Computer Architecture and Technology, Donostia-San Sebastian, Spain

    Tania Lorido-Botran & Jose Miguel-Alonso

  3. Department of Computer Science and Artificial Intelligence, Donostia, Gipuzkoa, Spain

    Jose A. Lozano

Authors
  1. Tania Lorido-Botran
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jose Miguel-Alonso
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jose A. Lozano
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Tania Lorido-Botran.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lorido-Botran, T., Miguel-Alonso, J. & Lozano, J.A. A Review of Auto-scaling Techniques for Elastic Applications in Cloud Environments. J Grid Computing 12, 559–592 (2014). https://doi.org/10.1007/s10723-014-9314-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10723-014-9314-7

Keywords

Search

Navigation