CFP last date
20 May 2024
Reseach Article

Cost Aware Dynamic Rule based Auto-scaling of Infrastructure as a Service in Cloud Environment

Published on February 2015 by M. Kriushanth, L. Arockiam
Advanced Computing and Communication Techniques for High Performance Applications
Foundation of Computer Science USA
ICACCTHPA2014 - Number 4
February 2015
Authors: M. Kriushanth, L. Arockiam
82b92423-3e86-43d9-964e-7059b7e31772

M. Kriushanth, L. Arockiam . Cost Aware Dynamic Rule based Auto-scaling of Infrastructure as a Service in Cloud Environment. Advanced Computing and Communication Techniques for High Performance Applications. ICACCTHPA2014, 4 (February 2015), 35-40.

@article{
author = { M. Kriushanth, L. Arockiam },
title = { Cost Aware Dynamic Rule based Auto-scaling of Infrastructure as a Service in Cloud Environment },
journal = { Advanced Computing and Communication Techniques for High Performance Applications },
issue_date = { February 2015 },
volume = { ICACCTHPA2014 },
number = { 4 },
month = { February },
year = { 2015 },
issn = 0975-8887,
pages = { 35-40 },
numpages = 6,
url = { /proceedings/icaccthpa2014/number4/19458-6047/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 Advanced Computing and Communication Techniques for High Performance Applications
%A M. Kriushanth
%A L. Arockiam
%T Cost Aware Dynamic Rule based Auto-scaling of Infrastructure as a Service in Cloud Environment
%J Advanced Computing and Communication Techniques for High Performance Applications
%@ 0975-8887
%V ICACCTHPA2014
%N 4
%P 35-40
%D 2015
%I International Journal of Computer Applications
Abstract

Cloud computing is one of the fastest growing technology. Pay-as-you-go model attracts the customer to utilize the large amount of cloud services in very low cost. Scalability and virtualization plays a vital role to achieve this goal. Scalability is the ability to find the number of users and to provide the service accordingly. Scaling can be divided into two, namely Auto-scaling or dynamic scaling and manual scaling. Auto-scaling doing great job to reduce the manual process. Scaling definitely reduces the service and operational cost, badly configured scaling sometimes increases the cost also. In such case there are chances for Service Level Agreement (SLA) violations and poor Quality of service (QoS). The perfect scaling should increase the profit for the Cloud Service Provider (CSP) and reduces the service cost, should not affect the QoS and SLA violations. In this paper, a dynamic rule based auto-scaling mechanism is proposed to reduce the cost of the VM instances.

References
  1. "Amazon Auto Scaling in Cloud Computing", http://aws. amazon. com/autoscaling/30. 05. 2013.
  2. https://developers. google. com/compute/pricing
  3. "Amazon EC2 pricing ", https://aws. amazon. com/ec2/pricing
  4. Z. Hill, J. Li, M. Mao, A. Ruiz-Alvarez and M. Humphrey, "Early Observations on the Performance of Windows Azure", Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing. Chicago, Illinois, June 21, 2010.
  5. H. Lim, S. Babu, J. Chase, and S. Parekh, "Automated Control in Cloud Computing: Challenges and Opportunities", 1st Workshop on Automated Control for Datacenters and Clouds, June 2009.
  6. P. Padala, K. Shin, X. Zhu, M. Uysal, Z. Wang, S. Singhal, A. Merchant, and K. Salem. 2007. Adaptive Control of Virtualized Resources in Utility Computing Environments. EuroSys, 2007.
  7. B. Urgaonkar, P. Shenoy, A. Chandra, and P. Goyal, "Dynamic provisioning of multi-tier internet applications", 2nd International Conference on Autonomic Computing, Seattle, WA, USA, June 2005.
  8. Q. Zhang, L. Cherkasova, E. Smirni, "A Regression-Based Analytic Model for Dynamic Resource Provisioning of Multi-Tier Applications", Proceedings of the Fourth International Conference on Autonomic Computing, Jacksonville, Florida, USA, June 2007.
  9. A. Chandra, W. Gong and P. Shenoy, "Dynamic Resource Allocation for Shared data centers using online measurements", Proceedings of the 11th International Workshop on Quality of Service, 2003.
  10. J. Chase, D. Irwin, L. Grit, J. Moore, and S. Sprenkle, "Dynamic Virtual Clusters in A Grid Site Manager", Proceedings of the 12th High Performance Distributed Computing, Seattle, Washington, June 22-24, 2003.
  11. S. Park and M. Humphrey, "Feedback-Controlled Resource Sharing for Predictable eScience", Proceedings IEEE/ACM International Conference for High Performance Computing, Networking, Storage and Analysis (SC08),Austin, Texas, 2008.
  12. S. Park and M. Humphrey, "Predictable High Performance Computing using Feedback Control and Admission Control", IEEE Transactions on Parallel and Distributed Systems (TPDS), Mar, 2010.
  13. P. Ruth, P. McGachey and D. Xu, "VioCluster: Virtualization for Dynamic Computational Domains", Cluster Computing, IEEE International, pages 1-10, Sep 2005.
  14. Y. Yazir, C. Matthews, R. Farahbod, S. Neville, "Dynamic Resource Allocation in Computing Clouds using Distributed Multiple Criteria Decision Analysis", 3rd International Conference on Cloud Computing, Miami, Florida, USA, 2010.
  15. M. Mazzucco, D. Dyachuk and R. Deters, "Maximizing Cloud Providers Revenues via Energy Aware Allocation Policies", 3rd International Conference on Cloud Computing, Miami, Florida, USA, 2010.
  16. I. Goiri, J. Guitart and J. Torres, "Characterizing Cloud Federation for Enhancing Providers' Profit", 3rd International Conference on Cloud Computing, Miami, Florida, USA, 2010.
  17. F. Chang, J. Ren and R. Viswanathan, "Optimal Resource Allocation in Clouds", 3rd International Conference on Cloud Computing, Miami, Florida, USA, 2010.
  18. E. Deelman, G. Singh, M. Livny, B Berriman, and J. Good, "The Cost of Doing Science on the Cloud: The montage example", Proceeding SC '08 Proceedings of the 2008 ACM/IEEE conference on Supercomputing. pp. 1-12. 2008.
  19. RightScale. http://rightscale. com
  20. enStratus. http://www. enstratus. com
  21. Scalr. https://www. scalr. net
  22. Ming Mao, Jie Li and Marty Humphery, "Cloud Auto-scaling with Deadline and Budget Constraints", Proceedings of 11th IEEE/ACM International Conference on Grid Computing, 2010, ISBN 978-1-4244-9349-4, pp 41-48.
  23. Ming Mao and Marty Humphery, "Auto-scaling to Minimize Cost and Meet Application Deadline in Cloud Workflows", Proceedings of International Conference for High Performance Computing, Networking, Storage and Analysis, IEEE 2011, E-ISBN 978-1-4503-0771-0, pp 1-12.
  24. Tania Lorido-Botran, Jose Miguel-Alonso and Jose A. Lozano,"Auto-scaling Techniques for Elastic Applications in Cloud Environments", Technical Report EHU-KAT-IK-09-12, Department of Computer Architecture and Technology, University of the Basque Country, September 5, 2012, pp1-44.
  25. Jingqi Yang, Chuanchang Liu, Yanlei Shang, Bo Cheng, Zexiang Mao, Chunhog Liu, Lisha Niu and Junliang Chen, "A cost-aware auto-scaling approach using the workload predection in service clouds", Springer 2013.
  26. Qi Zhang, Lu Cheng and Raouf Boutaba, "Cloud Computing: state-of-the-art and research challenges", Springer, April 2010, pp 7-18.
  27. Ming Mao and Marty Humphery, "A Performance Study on the VM Startup time in the Cloud", IEEE 5th International Conference on Cloud Computing, IEEE Computer Society, 2012, pp 423-430.
  28. "Microsoft instance types", http://msdn. microsoft. com/en-us/library/azure/dn197896. aspx/ 30. 04. 2013
  29. "Rackspace instance types", http://www. rackspace. com/cloud/servers/pricing/ 30. 04. 2013.
  30. AWS available instance types https://docs. aws. amazon. com/AWSEC2/latest/UserGuide/instance-types. html.
  31. Tania Lorido-Botran, JoseMiguel-Alonso and Jose A. Lozano," Comparison of Auto-scaling Techniques for Cloud Environment", In. Proc. of CDEI, 2013, ISBN: 978-84-695-8330-2.
Index Terms

Computer Science
Information Sciences

Keywords

Cloud Computing Auto-scaling Virtualization Quality Of Service And Service Level Agreements.