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RHAS: robust hybrid auto-scaling for web applications in cloud computing

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Abstract

The elasticity characteristic of cloud services attracts application providers to deploy applications in a cloud environment. The scalability feature of cloud computing gives the facility to application providers to dynamically provision the computing power and storage capacity from cloud data centers. The consolidation of services to few active servers can enhance the service sustainability and reduce the operational cost. The state-of-art algorithms mostly focus either on reactive or proactive auto-scaling techniques. In this article, a Robust Hybrid Auto-Scaler (RHAS) is presented for web applications. The time series forecasting model has been used to predict the future incoming workload. The reactive approach is used to deal with the current resource requirement. The proposed auto-scaling technique is designed with the threshold-based rules and queuing model. The security mechanism is used to secure the user’s request and response to the web-applications deployed in cloud environment. The designed approach has been tested with two real-time web application workloads of ClarkNet and NASA. The proposed technique achieves \(14\%\) reduction in cost, and significant improvement in response time, service level agreement (SLA) violation, and gives consistency in CPU utilization.

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References

  1. Adler, B.: Building Scalable Applications in the Cloud: Reference Architecture & Best Practices. Rightscale inc, Santa Barbara (2011)

    Google Scholar 

  2. Amiri, M., Mohammad-Khanli, L.: Survey on prediction models of applications for resources provisioning in cloud. J. Netw. Comput. Appl. 82, 93–113 (2017)

    Google Scholar 

  3. Aslanpour, M.S., Dashti, S.E.: Proactive auto-scaling algorithm (pasa) for cloud application. Int. J. Grid High Perform. Comput. 9(3), 1–16 (2017)

    Google Scholar 

  4. Aslanpour, M.S., Ghobaei-Arani, M., Toosi, A.N.: Auto-scaling web applications in clouds: a cost-aware approach. J. Netw. Comput. Appl. 95, 26–41 (2017)

    Google Scholar 

  5. Bodik, P., Griffith, R., Sutton, C., Fox, A., Jordan, M.I., Patterson, D.A.: Automatic exploration of datacenter performance regimes. In: Proceedings of the 1st workshop on Automated control for datacenters and clouds, pp. 1–6. ACM (2009)

  6. Bodík, P., Griffith, R., Sutton, C., Fox, A., Jordan, M.I., Patterson, D.A.: Statistical machine learning makes automatic control practical for internet datacenters. HotCloud 9, 12 (2009)

    Google Scholar 

  7. Calheiros, R.N., Masoumi, E., Ranjan, R., Buyya, R.: Workload prediction using arima model and its impact on cloud applications’ qos. IEEE Trans. Cloud Comput. 3(4), 449–458 (2015)

    Google Scholar 

  8. 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)

    Google Scholar 

  9. Casalicchio, E.: A study on performance measures for auto-scaling cpu-intensive containerized applications. Clust. Comput. 22(3), 995–1006 (2019)

    MathSciNet  Google Scholar 

  10. Casalicchio, E., Lundberg, L., Shirinbab, S.: Energy-aware auto-scaling algorithms for cassandra virtual data centers. Cluster Computing 20(3), 2065–2082 (2017)

    Google Scholar 

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

  12. 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. NSDI 8, 337–350 (2008)

    Google Scholar 

  13. Computing, A., et al.: An architectural blueprint for autonomic computing. IBM White Paper 31, 1–6 (2006)

    Google Scholar 

  14. Coulson, N.C., Sotiriadis, S., Bessis, N.: Adaptive microservice scaling for elastic applications. IEEE Internet Things J. 7(5), 4195–4202 (2020)

    Google Scholar 

  15. Coutinho, E.F., de Carvalho Sousa, F.R., Rego, P.A.L., Gomes, D.G., de Souza, J.N.: Elasticity in cloud computing: a survey. Ann. Telecommun. 70(7–8), 289–309 (2015)

    Google Scholar 

  16. Cui, H., Yi, X., Nepal, S.: Achieving scalable access control over encrypted data for edge computing networks. IEEE Access 6, 30049–30059 (2018)

    Google Scholar 

  17. De Grande, R.E., Boukerche, A., Alkharboush, R.: Time series-oriented load prediction model and migration policies for distributed simulation systems. IEEE Trans. Parallel Distrib. Syst. 28(1), 215–229 (2017)

    Google Scholar 

  18. Dutta, S., Gera, S., Verma, A., Viswanathan, B.: Smartscale: Automatic application scaling in enterprise clouds. In: Cloud Computing (CLOUD), 2012 IEEE 5th International Conference on, pp. 221–228. IEEE (2012)

  19. EC2”, A.: ”spot instances” (2018). https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/using-spot-instances.html

  20. Elumalaivasan, P., Kulothungan, K., Sannasi, G., Arputharaj, K.: Trust based ciphertext policy attribute based encryption techniques for decentralized disruption tolerant networks. Aust. J. Basic Appl. Sci 10, 18–26 (2016)

    Google Scholar 

  21. Erradi, A., Iqbal, W., Mahmood, A., Bouguettaya, A.: Web application resource requirements estimation based on the workload latent features. IEEE Trans. Serv. Comput. (2019)

  22. Fallah, M., Arani, M.G., Maeen, M.: Nasla: novel auto scaling approach based on learning automata for web application in cloud computing environment. Int. J. Comput. Appl. 113(2), 18–23 (2015)

    Google Scholar 

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

  24. Fernandez, H., Pierre, G., Kielmann, T.: Autoscaling web applications in heterogeneous cloud infrastructures. In: Cloud Engineering (IC2E), 2014 IEEE International Conference on, pp. 195–204. IEEE (2014)

  25. Ganapathy, S., et al.: A secured storage and privacy-preserving model using crt for providing security on cloud and iot-based applications. Comput. Netw. 151, 181–190 (2019)

    Google Scholar 

  26. Garí, Y., Monge, D.A., Mateos, C., Garino, C.G.: Learning budget assignment policies for autoscaling scientific workflows in the cloud. Clust. Comput. 23(1), 87–105 (2020)

    Google Scholar 

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

  28. Gill, S.S., Chana, I., Singh, M., Buyya, R.: Chopper: an intelligent qos-aware autonomic resource management approach for cloud computing. Clust. Comput. 21(2), 1203–1241 (2018)

    Google Scholar 

  29. Gill, S.S., Tuli, S., Xu, M., Singh, I., Singh, K.V., Lindsay, D., Tuli, S., Smirnova, D., Singh, M., Jain, U., et al.: Transformative effects of IoT, blockchain and artificial intelligence on cloud computing: evolution, vision, trends and open challenges. Internet of Things 8, 100118 (2019)

    Google Scholar 

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

  31. Han, R., Ghanem, M.M., Guo, L., Guo, Y., Osmond, M.: Enabling cost-aware and adaptive elasticity of multi-tier cloud applications. Fut. Gener. Comput. Syst. 32, 82–98 (2014)

    Google Scholar 

  32. Hashmi, K., Malik, Z., Erradi, A., Rezgui, A.: Qos dependency modeling for composite systems. IEEE Transactions on Services Computing 11(6), 936–947 (2016)

    Google Scholar 

  33. Hu, S., Smith, J.E.: Reducing startup time in co-designed virtual machines. In: 33rd International Symposium on Computer Architecture (ISCA’06), pp. 277–288. IEEE (2006)

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

  35. Iqbal, W., Dailey, M.N., Carrera, D.: Low cost quality aware multi-tier application hosting on the amazon cloud. In: 2014 International Conference on Future Internet of Things and Cloud, pp. 202–209. IEEE (2014)

  36. Iqbal, W., Dailey, M.N., Carrera, D.: Unsupervised learning of dynamic resource provisioning policies for cloud-hosted multitier web applications. IEEE Syst. J. 10(4), 1435–1446 (2015)

    Google Scholar 

  37. Iqbal, W., Dailey, M.N., Carrera, D., Janecek, P.: Adaptive resource provisioning for read intensive multi-tier applications in the cloud. Fut. Gener. Comput. Syst. 27(6), 871–879 (2011)

    Google Scholar 

  38. Islam, S., Keung, J., Lee, K., Liu, A.: Empirical prediction models for adaptive resource provisioning in the cloud. Fut. Gener. Comput. Syst. 28(1), 155–162 (2012)

    Google Scholar 

  39. Kavin, B.P., Ganapathy, S., Karman, A.: An intelligent task scheduling approach for cloud using ipso and a* search algorithm. In: 2018 Eleventh International Conference on Contemporary Computing (IC3), pp. 1–5. IEEE (2018)

  40. Kim, H., el Khamra, Y., Jha, S., Parashar, M.: An autonomic approach to integrated hpc grid and cloud usage. In: e-Science, 2009. e-Science’09. Fifth IEEE International Conference on, pp. 366–373. IEEE (2009)

  41. Kupferman, J.: Scaling into the cloud. CS270 Advanced Operating Systems, 2009 (2009)

  42. Li, J., Su, S., Cheng, X., Song, M., Ma, L., Wang, J.: Cost-efficient coordinated scheduling for leasing cloud resources on hybrid workloads. Parallel Comput. 44, 1–17 (2015)

    MathSciNet  Google Scholar 

  43. Lim, H.C., Babu, S., Chase, J.S.: Automated control for elastic storage. In: Proceedings of the 7th international conference on Autonomic computing, pp. 1–10. ACM (2010)

  44. Lin, W.: Study on the design and application of the user information resources of track and field web course based on software programming method. Clust. Comput. 22(6), 15295–15303 (2019)

    Google Scholar 

  45. Liu, J., Zhang, Y., Zhou, Y., Zhang, D., Liu, H.: Aggressive resource provisioning for ensuring qos in virtualized environments. IEEE Trans. Cloud Comput. 1, 1–1 (2015)

    Google Scholar 

  46. Lorido-Botran, T., Miguel-Alonso, J., Lozano, J.A.: A review of auto-scaling techniques for elastic applications in cloud environments. J. Grid Comput. 12(4), 559–592 (2014)

    Google Scholar 

  47. Mao, M., Humphrey, M.: Auto-scaling to minimize cost and meet application deadlines in cloud workflows. In: High Performance Computing, Networking, Storage and Analysis (SC), 2011 International Conference for, pp. 1–12. IEEE (2011)

  48. Mao, M., Humphrey, M.: A performance study on the vm startup time in the cloud. In: 2012 IEEE 5th international conference on Cloud Computing (CLOUD), pp. 423–430. IEEE (2012)

  49. Maurer, M., Breskovic, I., Emeakaroha, V.C., Brandic, I.: Revealing the mape loop for the autonomic management of cloud infrastructures. In: Computers and Communications (ISCC), 2011 IEEE Symposium on, pp. 147–152. IEEE (2011)

  50. Messias, V.R., Estrella, J.C., Ehlers, R., Santana, M.J., Santana, R.C., Reiff-Marganiec, S.: Combining time series prediction models using genetic algorithm to autoscaling web applications hosted in the cloud infrastructure. Neural Comput. Appl. 27(8), 2383–2406 (2016)

    Google Scholar 

  51. 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. IEEE (2010)

  52. Muthurajkumar, S., Ganapathy, S., Vijayalakshmi, M., Kannan, A.: Secured temporal log management techniques for cloud. Proc. Comput. Sci. 46, 589–595 (2015)

    Google Scholar 

  53. Muthurajkumar, S., Vijayalakshmi, M., Kannan, A., Ganapathy, S.: Optimal and energy efficient scheduling techniques for resource management in public cloud networks. Natl. Acad. Sci. Lett. 41(4), 219–223 (2018)

    MathSciNet  Google Scholar 

  54. Nikravesh, A.Y., Ajila, S.A., Lung, C.H.: Towards an autonomic auto-scaling prediction system for cloud resource provisioning. In: Proceedings of the 10th international symposium on software engineering for adaptive and self-managing systems, pp. 35–45. IEEE Press (2015)

  55. Padhy, N., Singh, R., Satapathy, S.C.: Cost-effective and fault-resilient reusability prediction model by using adaptive genetic algorithm based neural network for web-of-service applications. Clust. Comput. 22(6), 14559–14581 (2019)

    Google Scholar 

  56. Park, S.M., Humphrey, M.: Self-tuning virtual machines for predictable escience. In: Proceedings of the 2009 9th IEEE/ACM international symposium on cluster computing and the grid, pp. 356–363. IEEE Computer Society (2009)

  57. Prodan, R., Nae, V.: Prediction-based real-time resource provisioning for massively multiplayer online games. Future Generation Computer Systems 25(7), 785–793 (2009)

    Google Scholar 

  58. Qu, C., Calheiros, R.N., Buyya, R.: Auto-scaling web applications in clouds: a taxonomy and survey. arXiv:1609.09224 (2016)

  59. Roy, N., Dubey, A., Gokhale, A.: Efficient autoscaling in the cloud using predictive models for workload forecasting. In: 2011 IEEE International Conference on Cloud computing (CLOUD), pp. 500–507. IEEE (2011)

  60. Sedaghat, M., Hernandez-Rodriguez, F., Elmroth, E.: A virtual machine re-packing approach to the horizontal vs. vertical elasticity trade-off for cloud autoscaling. In: Proceedings of the 2013 ACM cloud and autonomic computing conference, p. 6. ACM (2013)

  61. Shen, Y., Chen, H., Shen, L., Mei, C., Pu, X.: Cost-optimized resource provision for cloud applications. In: High Performance Computing and Communications, 2014 IEEE 6th Intl Symp on Cyberspace Safety and Security, 2014 IEEE 11th Intl Conf on Embedded Software and Syst (HPCC, CSS, ICESS), 2014 IEEE Intl Conf on, pp. 1060–1067. IEEE (2014)

  62. Shen, Z., Subbiah, S., Gu, X., Wilkes, J.: Cloudscale: elastic resource scaling for multi-tenant cloud systems. In: Proceedings of the 2nd ACM Symposium on Cloud Computing, p. 5. ACM (2011)

  63. Singh, P., Gupta, P., Jyoti, K.: Tasm: technocrat arima and svr model for workload prediction of web applications in cloud. Clust. Comput. 22(2), 619–633 (2019)

    Google Scholar 

  64. Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: issues and challenges. J. Grid Comput. 14(2), 217–264 (2016)

    Google Scholar 

  65. Subbulakshmi, P., Sumathi, V., Ganapathy, S.: Cloud based pos system for secured smart shopping cart using rfid. J. Adv. Res. Dyn. Control Syst. 9(Sp–14), 2764–2777 (2017)

    Google Scholar 

  66. Vijayakumar, P., Bose, S., Kannan, A.: Chinese remainder theorem based centralised group key management for secure multicast communication. IET Inf. Secur. 8(3), 179–187 (2014)

    Google Scholar 

  67. Xiang, S., He, J.: Database authentication watermarking scheme in encrypted domain. IET Inf. Secur. 12(1), 42–51 (2017)

    Google Scholar 

  68. Yang, R., Hu, C., Sun, X., Garraghan, P., Wo, T., Wen, Z., Peng, H., Xu, J., Li, C.: Performance-aware speculative resource oversubscription for large-scale clusters. IEEE Trans. Parallel Distrib. Syst. 31(7), 1499–1517 (2020)

    Google Scholar 

  69. Yazdanov, L., Fetzer, C.: Lightweight automatic resource scaling for multi-tier web applications. In: 2014 IEEE 7th International Conference on Cloud Computing, pp. 466–473. IEEE (2014)

  70. You, G., Wang, X.: A server-side accelerator framework for multi-core cpus and intel xeon phi co-processor systems. Clust. Comput. pp. 1–18 (2020)

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Authors and Affiliations

  1. School of Computer Science and Engineering, Lovey Professional University, Phagwara, Punjab, India

    Parminder Singh, Avinash Kaur & Pooja Gupta

  2. School of Electronic Engineering and Computer Science, Queen Mary University of London, London, UK

    Sukhpal Singh Gill

  3. Department of Information Technology, Guru Nanak Dev Engineering College, Ludhiana, Punjab, India

    Kiran Jyoti

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Singh, P., Kaur, A., Gupta, P. et al. RHAS: robust hybrid auto-scaling for web applications in cloud computing. Cluster Comput 24, 717–737 (2021). https://doi.org/10.1007/s10586-020-03148-5

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