A Review of Dynamic Resource Scaling applications in the Cloud Computing

  IJCTT-book-cover
 
International Journal of Computer Trends and Technology (IJCTT)          
 
© 2016 by IJCTT Journal
Volume-33 Number-2
Year of Publication : 2016
Authors : Dr. Amit Chaturvedi, Malay Upadhyay
  10.14445/22312803/IJCTT-V33P111

MLA

Dr. Amit Chaturvedi, Malay Upadhyay "A Review of Dynamic Resource Scaling applications in the Cloud Computing". International Journal of Computer Trends and Technology (IJCTT) V33(2):47-52, March 2016. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.

Abstract -
The degree of automation, abstraction for the user (service provider) and customization of the rules governing the service vary. Some systems offer users the chance of building rather simple conditions based on fixed infrastructure/platform metrics (e.g. CPU, memory, etc.), while others employ server-level metrics (e.g. cost to benefit ratio) and allow for more complex conditions (e.g. arithmetic and logic combinations of simple rules) to be included in the rules. Scalability of resources as per the requirement is said to be one of the major advantages brought by the cloud paradigm and, more specifically, the one that makes it different to an “advanced outsourcing” solutions. However, there are some important pending issues before making the dynamic resource scaling for applications come true. In this paper, the most notable initiatives towards whole application scalability in cloud environments are presented. We present relevant efforts at the edge of state of the art technology, providing an encompassing overview of the trends they each follow. We also highlight pending challenges that will likely be addressed in new research efforts and present an ideal scalable cloud system.

References
[1] L.M. Vaquero, L.R.Marino, R.k. Buyya, “Dynamic Scaling Application in the Cloud”, ACM SIGCOMM Computer Communication Review, Vol. 41, No. 1, Jan 2011
[2] Amazon elastic compute cloud (EC2): http://aws.amazon.com/ec2/ (29.10.2011).
[3] GoGrid: http://www.gogrid.com/ (29.10. 2011).
[4] RightScale: http://www.rightscale.com/ (29.10. 2011).
[5] D. A. Bacigalupo, J. van Hemert, et al, "Managing dynamic enterprise and urgent workloads on clouds using layered queuing and historical performance models," Simulation Modelling Practice and Theory, vol. 19, pp. 1479-1495, 2011.
[6] J. Bi, Z. Zhu, R. Tian, and Q. Wang, "Dynamic Provisioning Modeling for Virtualized Multi-tier Applications in Cloud Data Center," in CLOUD’10, Miami, Florida, 2010, pp. 370-377.
[7] W. Iqbal, M. N. Dailey, D. Carrera, and P. Janecek, "Adaptive resource provisioning for read intensive multi-tier applications in the cloud," Future Generation Computer Systems, vol. 27, pp. 871-879, 2011.
[8] J. Z. Li, "Fast Optimization for Scalable Application Deployments in Large Service Centers," Doctor of Philosophy thesis, Department of Systems and Computer Engineerin, Carleton University, 2011.
[9] Han R., Guo L.,Ghanem M., Guo Y. “Lightweight Resource Scaling for Cloud Applicatoin”, 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, 2012, pp. 644-651.
[10] S. He, L. Guo, Y. Guo, M. Ghanem, R. Han, and C. Wu, "Elastic Application Container: A Lightweight Approach for Cloud Resource Provisioning," in The 26th IEEE International Conference on Advanced Information Networking and Applications (AINA-2012), Fukuoka, Japan, 2012, in press.
[11] T. Hirofuchi, H. Nakada, S. Itoh, and S. Sekiguchi, "Enabling instantaneous relocation of virtual machines with a lightweight VMM extension," in 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing (CCGrid'11), Melbourne, VIC, Australia, 2010, pp. 73-83,
[12] S. Das, S. Nishimura, D. Agrawal, and A. El Abbadi, "Albatross: lightweight elasticity in shared storage databases for the cloud using live data migration," Proceedings of the VLDB Endowment, vol. 4, pp. 494-505, 2011.
[13] X. Fan, W.-D.Weber, and L. A. Barroso. Power provisioningfor a warehouse-sized computer. In Proc. ISCA, 2007. 2008the System S declarative stream processing engine
[14] D. Gmach, J. Rolia, L. Cherkasova, and A. Kemper. Capacitymanagement and demand prediction for next generation datacenters. In Proc. ICWS, 2007.
[15] E. Kalyvianaki, T. Charalambous, and S. Hand. Self-adaptiveand self-configured CPU resource provisioning forvirtualized servers using Kalman filters. In Proc. ICAC,2009.
[16] H. Lim, S. Babu, and J. Chase. Automated control for elasticstorage. In Proc. ICAC, 2010.
[17] Xiaoyun Zhu, Zhikui Wang, SharadSinghal Utility-driven workloadmanagement using nested control design. In Proc. AmericanControl Conference, 2006.
[18] B. Urgaonkar, M. S. G. Pacifici, P. J. Shenoy, and A. N.Tantawi. An analytical model for multi-tier internet servicesand its applications. In Proc. SIGMETRICS, 2005.
[19] Z. Gong, X. Gu, and J. Wilkes. PRESS: PRedictive ElasticReSource Scaling for Cloud Systems.In Proc. CNSM, 2010.
[20] M. Armbrust, A. Fox, D. A. Patterson, N. Lanham,B. Trushkowsky, J. Trutna, and H. Oh. Scads:Scale-independent storage for social computing applications.In Proc. CIDR, 2009.
[21] ZhimingShen, SethuramanSubbiah, Xiaohui Gu, John Wilkes, CloudScale: Elastic Resource Scaling for Multi-Tenant Cloud Systems 2011
[22] Djamal Ziani1,configuration in erpsaas multi-tenancy,
[23] VenkatanathanVaradarajan†, Yinqian Zhang‡, Thomas Ristenpart_, and Michael Swift†,Placement Vulnerability Study in Multi-Tenant Public Clouds
[24] Jing Zhu_, Dan Li_z, Jianping Wu_, Hongnan Liu_, Ying Zhangy, JingchengZhang_Towards Bandwidth Guarantee in MultitenancyCloud Computing Networks
[25] Twinkle Garg1, Rajender Kumar2, JagtarSinghA way to cloud computing basic to multitenant environment

Keywords
Cloud Computing, dynamic resource scaling, Virtual Machine (VM).