A Novel Approach for Optimization Auto-Scaling in Cloud Computing Environment

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Author(s)

Khosro Mogouie 1,* Mostafa Ghobaei Arani 2 Mahboubeh Shamsi 3

1. Department of Computer Engineering, Mahallat Branch, Islamic Azad University, Mahallat, Iran

2. Department of Computer Engineering, Parand Branch, Islamic Azad University, Tehran, Iran

3. Department of Computer Engineering, Qom Branch, University Of Technology Qom, Iran

* Corresponding author.

DOI: https://doi.org/10.5815/ijcnis.2015.11.05

Received: 12 Feb. 2015 / Revised: 23 Jun. 2015 / Accepted: 10 Aug. 2015 / Published: 8 Oct. 2015

Index Terms

Cloud computing, Scalability, Auto-scaling, Learning automata

Abstract

In recent years, applications of cloud services have been increasingly expanded. Cloud services, are distributed infrastructures which develop the communication and services. Auto scaling is one of the most important features of cloud services which dedicates and retakes the allocated dynamic resource in proportion to the volume of requests. Scaling tries to utilize maximum power of the available resources also to use idle resources, in order to maximize the efficiency or shut down unnecessary resources to reduce the cost of running requests. In this paper, we have suggested an approach based on learning automata auto- scaling, in order to manage and optimize factors like cost, rate of violations of user-level agreements (SLA Violation) as well as stability in the presence of traffic workload. Results of simulation show that proposed approach has been able to optimize cost and rate of SLA violation in order to manage their trade off. Also, it decreases number of operation needed for scaling to increase stability of system compared to the other approaches.

Cite This Paper

Khosro Mogouie, Mostafa Ghobaei Arani, Mahboubeh Shamsi, "A Novel Approach for Optimization Auto-Scaling in Cloud Computing Environment", International Journal of Computer Network and Information Security(IJCNIS), vol.7, no.11, pp.46-53, 2015. DOI:10.5815/ijcnis.2015.11.05

Reference

[1]Anandhi, R. and K. Chitra. "A challenge in improving the consistency of transactions in cloud databases-scalability." International Journal of Computer Applications 52, no. 2 (2012): 12-14.
[2]Afife Fereydooni, Mostafa Ghobaei Arani and Mahboubeh Shamsi "EDLT: An Extended DLT to Enhance Load Balancing in Cloud Computing." International Journal of Computer Applications 108(7):6-11, December 2014.
[3]Md. Imran Alam, Manjusha Pandey, Siddharth S Rautaray,"A Comprehensive Survey on Cloud Computing", IJITCS, vol.7, no.2, pp.68-79, 2015. DOI: 10.5815/ijitcs.2015.02.09.
[4]Vaquero, Luis M., Luis Rodero-Merino and Rajkumar Buyya. "Dynamically scaling applications in the cloud." ACM SIGCOMM Computer Communication Review 41, no. 1 (2011): 45-52.
[5]Mao, Ming, Jie Li and Marty Humphrey. "Cloud auto-scaling with deadline and budget constraints." In Grid Computing (GRID), 2010 11th IEEE/ACM International Conference on, pp. 41-48. IEEE, 2010.
[6]Mao.Ming and Marty Humphrey. "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, p. 49. ACM, 2011.
[7]Monireh Fallah, Mostafa Ghobaei Arani and Mehrdad Maeen. "NASLA: Novel Auto Scaling Approach based on Learning Automata for Web Application in Cloud Computing Environment." International Journal of Computer Applications 113(2):18-23, March 2015.
[8]Monireh Fallah, Mostafa Ghobaei Arani "ASTAW: Auto-Scaling Threshold-based Approach for Web Application in Cloud Computing Environment." International Journal of u- and e- Service, Science and Technology (IJUNESST),Vol.8, No.3, pp.221-230, 2015.
[9]Hasan Masum Z., Edgar Magana, Alexander Clemm, Lew Tucker and Sree Lakshmi D. Gudreddi. "Integrated and autonomic cloud resource scaling." InNetwork Operations and Management Symposium (NOMS)? 2012 IEEE? pp. 1327-1334.
[10]Han Rui, Li Guo, Moustafa M. Ghanem and YikeGuo. "Lightweight resource scaling for cloud applications." In Cluster? Cloud and Grid Computing (CCGrid), 2012 12th IEEE/ACM International Symposium on, pp. 644-651.
[11]Maurer Michael, Ivona Brandic and RizosSakellariou. "Enacting SLA’s in clouds using rules." In Euro-Par 2011 Parallel Processing, pp. 455-466. Springer Berlin Heidelberg, 2011.
[12]Dutreilh Xavier, Nicolas Rivierre, Aurélien Moreau? Jacques Malenfant and Isis Truck. "From data center resource allocation to control theory and back." In Cloud Computing (CLOUD) 2010 IEEE 3rd International Conference on? pp. 410-417.
[13]Narendra, Kumpati S. and Mandayam AL Thathachar. "Learning automata: an introduction." Courier Corporation, 2012.
[14]D. Menasc and E. Casalicchio. 2004. "A Framework for Resource Allocation in Grid Computing." In Proc. of the 12th Annual International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunications Systems, pp. 259-267, 2004.
[15]N. Chohan, C. Castillo, M. Spreitzer, M. Steinder, A. Tantawi and C. Krintz. 2010. See Spot Run: Using Spot Instances for MapReduce Workflows. In 2nd USENIX Workshop on Hot Topics in Cloud Computing, HotCloud2010. Boston, MA. June 2010.
[16]Y. Yazir, C. Matthews, R. Farahbod, S. Neville, etc. 2010. "Dynamic Resource Allocation in Computing Clouds using Distributed Multiple Criteria Decision Analysis." In 3rd International Conference on Cloud Computing, Miami, Florida, USA, 2010.
[17]Ro.Nilabja, AbhishekDubey, and AniruddhaGokhale. "Efficient autoscaling in the cloud using predictive models for workload forecasting." In Cloud Computing (CLOUD), 2011 IEEE International Conference on, pp. 500-507. IEEE, 2011.
[18]S. Abdelwahed, J. Bai, R. Su, and N. Kandasamy, "On the application of predictive control techniques for adaptive performance management of computing systems", Network and Service Management, IEEE Transactions on, vol. 6, no. 4, pp. 212 –225, dec. 2009.
[19]S. Abdelwahed, N. Kandasamy, and S. Neema, "A controlbased framework for self-managing distributed computing systems," in WOSS ’04: Proceedings of the 1st ACM SIGSOFT workshop on Self-managed systems. New York, NY, USA: ACM, 2004, pp. 3–7.
[20]Chieu, Trieu C., Ajay Mohindra, Alexei A. Karve, and Alla Segal. "Dynamic scaling of web applications in a virtualized cloud computing environment." In e-Business Engineering, 2009. ICEBE'09. IEEE International Conference on, pp. 281-286. IEEE, 2009.
[21]Yang, Jingqi, Chuanchang Liu, Yanlei Shang, Bo Cheng, Zexiang Mao, Chunhong Liu, LishaNiu, and Junliang Chen. "A cost-aware auto-scaling approach using the workload prediction in service clouds." Information Systems Frontiers 16, no. 1 (2014): 7-18.
[22]K. Narendra and M. A. L. Thathachar, "Learning Automata: An Introduction", Prentice Hall, Englewood Cliffs, New Jersey, 1989.
[23]K. Najim and A. S. Poznyak, "Learning Automata: Theory and Application", Tarrytown, NY: Elsevier Science Ltd., 1994.
[24]Behnaz Seyed Taheri, Mostafa Ghobaei Arani and Mehrdad Maeen. "ACCFLA: Access Control in Cloud Federation using Learning Automata." International Journal of Computer Applications 107(6):30-40, December 2014.
[25]Calheiros, R. N., Ranjan, R., Beloglazov, A., De Rose, C. A., & Buyya, R. (2011). "CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms." Software: Practice and Experience, 41(1), 23-50.
[26]Yang, Jingqi, Chuanchang Liu, Yanlei Shang, Bo Cheng, ZexiangMao, Chunhong Liu, LishaNiu, and Junliang Chen. "A cost-aware auto-scaling approach using the workload prediction in service clouds." Information Systems Frontiers 16, no. 1 (2014): 7-18.