A Comparative Analysis of Various Auto-Scalers in the Cloud Environment
Keywords:
Auto-scaling, Application Provisioning, Cloud ComputingAbstract
The IaaS service model offers resources to its customers in the form of virtual machines (VMs) on a pay per use basis. These days, large enterprises and even small and medium businesses (SMBs) have started deploying their applications on clouds due to the various advantages it offers. The elastic feature of the clouds lets the deployed applications to scale their resources in accordance with the workload demands. This ensures that the applications provide the guaranteed QoS to its users as specified in the SLAs. To handle the automatic acquiring and releasing of resources as per application workload demands in the cloud environment (auto-scaling), various techniques have been proposed by researchers in the past. This paper performs a comparative analysis of various auto scaling techniques in cloud with respect to a number of factors viz. scaling technique, scaling type, scaling timing, and workload nature.
References
- JoSEP, A. D., Katz, R., Konwinski, A., Gunho, L., PAttERSon, D., & RABKin, A. (2010). A view of cloud computing. Communications of the ACM, 53(4)
- "Animoto case in rightscale blog," http://blog.rightscale.com/2008/04/23/animoto-facebook-scale-up/
- Shoaib, Y., & Das, O. (2014). Performance-oriented Cloud Provisioning: Taxonomy and Survey.arXiv preprint arXiv:1411.5077
- Gandhi, A., Dube, P., Karve, A., Kochut, A., & Zhang, L. (2014, June). Adaptive, Model-driven Autoscaling for Cloud Applications. In ICAC(Vol. 14, pp. 57-64)
- Yazdanov, L., & Fetzer, C. (2012, November). Vertical scaling for prioritized vms provisioning. In Cloud and Green Computing (CGC), 2012 Second International Conference on(pp. 118-125). IEEE
- http://docs.aws.amazon.com/autoscaling/latest/userguide/WhatIsAutoScaling.html
- https://www.packtpub.com/books/content/elastic-load-balancing
- Loff, J., & Garcia, J. (2014, December). Vadara: Predictive elasticity for cloud applications. In Cloud Computing Technology and Science (CloudCom), 2014 IEEE 6th International Conference on(pp. 541-546). IEEE
- Amazon. 2016. Amazon Auto Scaling Service. (2016). http://aws.amazon.com/autoscaling/ Carlos Vazquez- time series
- Xu, C. Z., Rao, J., & Bu, X. (2012). URL: A unified reinforcement learning approach for autonomic cloud management. Journal of Parallel and Distributed Computing, 72(2), 95-105
- Dutreilh, X., Moreau, A., Malenfant, J., Rivierre, N., & Truck, I. (2010, July). From data center resource allocation to control theory and back. In Cloud Computing (CLOUD), 2010 IEEE 3rd International Conference on(pp. 410-417). IEEE
- Calheiros, R. N., Masoumi, E., Ranjan, R., & Buyya, R. (2015). Workload prediction using ARIMA model and its impact on cloud applications’ QoS. IEEE Transactions on Cloud Computing, 3(4), 449-458
- Calheiros, R. N., Ranjan, R., & Buyya, R. (2011, September). Virtual machine provisioning based on analytical performance and QoS in cloud computing environments. In Parallel processing (ICPP), 2011 international conference on(pp. 295-304). IEEE
- Ferretti, S., Ghini, V., Panzieri, F., Pellegrini, M., & Turrini, E. (2010, July). Qos–aware clouds. In Cloud Computing (CLOUD), 2010 IEEE 3rd International Conference on(pp. 321-328). IEEE
- Gong, Z., Gu, X., & Wilkes, J. (2010, October). Press: Predictive elastic resource scaling for cloud systems. In Network and Service Management (CNSM), 2010 International Conference on(pp. 9-16). IEEE
- Jiang, J., Lu, J., Zhang, G., & Long, G. (2013, May). Optimal cloud resource auto-scaling for web applications. In Cluster, Cloud and Grid Computing (CCGrid), 2013 13th IEEE/ACM International Symposium on(pp. 58-65). IEEE
- Roy, N., Dubey, A., & Gokhale, A. (2011, July). Efficient autoscaling in the cloud using predictive models for workload forecasting. In Cloud Computing (CLOUD), 2011 IEEE International Conference on(pp. 500-507). IEEE
- Fernandez, H., Pierre, G., & Kielmann, T. (2014, March). Autoscaling web applications in heterogeneous cloud infrastructures. In Cloud Engineering (IC2E), 2014 IEEE International Conference on(pp. 195-204). IEEE
- Nguyen, H., Shen, Z., Gu, X., Subbiah, S., & Wilkes, J. (2013, June). AGILE: Elastic Distributed Resource Scaling for Infrastructure-as-a-Service. In ICAC(Vol. 13, pp. 69-82)
- Urgaonkar, B., Shenoy, P., Chandra, A., Goyal, P., & Wood, T. (2008). Agile dynamic provisioning of multi-tier internet applications. ACM Transactions on Autonomous and Adaptive Systems (TAAS), 3(1), 1
- Zhang, Q., Cherkasova, L., & Smirni, E. (2007, June). A regression-based analytic model for dynamic resource provisioning of multi-tier applications. In Autonomic Computing, 2007. ICAC'07. Fourth International Conference on(pp. 27-27). IEEE
- Bankole, A. A., & Ajila, S. A. (2013, May). Predicting cloud resource provisioning using machine learning techniques. In Electrical and Computer Engineering (CCECE), 2013 26th Annual IEEE Canadian Conference on(pp. 1-4). IEEE
- Al-Ayyoub, M., Jararweh, Y., Daraghmeh, M., & Althebyan, Q. (2015). Multi-agent based dynamic resource provisioning and monitoring for cloud computing systems infrastructure. Cluster Computing, 18(2), 919-932
- Liu, J., Zhang, Y., Zhou, Y., Zhang, D., & Liu, H. (2015). Aggressive resource provisioning for ensuring QoS in virtualized environments. IEEE Transactions on Cloud Computing, 3(2), 119-131
- Ghobaei-Arani, M., Jabbehdari, S., & Pourmina, M. A. (2017). An autonomic resource provisioning approach for service-based cloud applications: A hybrid approach. Future Generation Computer Systems
- Tighe, M., & Bauer, M. (2014, May). Integrating cloud application autoscaling with dynamic vm allocation. In Network Operations and Management Symposium (NOMS), 2014 IEEE(pp. 1-9). IEEE
- Spinner, S., Kounev, S., Zhu, X., Lu, L., Uysal, M., Holler, A., & Griffith, R. (2014, September). Runtime vertical scaling of virtualized applications via online model estimation. In Self-Adaptive and Self-Organizing Systems (SASO), 2014 IEEE Eighth International Conference on(pp. 157-166). IEEE
- Grozev, N., & Buyya, R. (2016). Dynamic Selection of Virtual Machines for Application Servers in Cloud Environments. arXiv preprint arXiv:1602.02339
- Hirashima, Y., Yamasaki, K., & Nagura, M. (2016, July). Proactive-Reactive Auto-Scaling Mechanism for Unpredictable Load Change. In Advanced Applied Informatics (IIAI-AAI), 2016 5th IIAI International Congress on(pp. 861-866). IEEE
- Dupont, S., Lejeune, J., Alvares, F., & Ledoux, T. (2015, September). Experimental analysis on autonomic strategies for cloud elasticity. In Cloud and Autonomic Computing (ICCAC), 2015 International Conference on(pp. 81-92). IEEE
- Zhu, Q., & Agrawal, G. (2010, June). Resource provisioning with budget constraints for adaptive applications in cloud environments. In Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing(pp. 304-307). ACM
- Al-Dhuraibi, Y., Paraiso, F., Djarallah, N., & Merle, P. (2017). Elasticity in Cloud Computing: State of the Art and Research Challenges. IEEE Transactions on Services Computing
- Bankole, A. A., & Ajila, S. A. (2013, May). Predicting cloud resource provisioning using machine learning techniques. In Electrical and Computer Engineering (CCECE), 2013 26th Annual IEEE Canadian Conference on(pp. 1-4). IEEE
- Gong, Z., Gu, X., & Wilkes, J. (2010, October). Press: Predictive elastic resource scaling for cloud systems. In Network and Service Management (CNSM), 2010 International Conference on(pp. 9-16). IEEE
- Islam, S., Keung, J., Lee, K., & Liu, A. (2012). Empirical prediction models for adaptive resource provisioning in the cloud. Future Generation Computer Systems, 28(1), 155-162
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