International Journal of Computer
Trends and Technology

Research Article | Open Access | Download PDF

Volume 30 | Number 1 | Year 2015 | Article Id. IJCTT-V30P116 | DOI : https://doi.org/10.14445/22312803/IJCTT-V30P116

Adaptive resource scaling methods for Multi-tenant cloud system


Dr. Amit Chaturvedi, Zahoor Ahmad Bhat

Citation :

Dr. Amit Chaturvedi, Zahoor Ahmad Bhat, "Adaptive resource scaling methods for Multi-tenant cloud system," International Journal of Computer Trends and Technology (IJCTT), vol. 30, no. 1, pp. 93-97, 2015. Crossref, https://doi.org/10.14445/22312803/IJCTT-V30P116

Abstract

Cloud resource scaling is an important issue to address proper resource utilization in multi-tenant cloud computing environment. Analysis of Resource Scaling Infrastructure as Service is the main objective in this paper. Multitenant environment applications use virtualized technologies to encapsulate and segregate application performance by using separate virtual machines (VM). We have discussed three issue of resource scaling i) high resource scaling ii) low resource scaling iii) internet speed. So, we proposed that researcher should focus on developing such solutions that will allocate the resources dynamically and on demand. The solutions should be so dynamic, if some resources are occupied for a long time and another resource requirement occurs. The provider should adjust the balance from the already booked resource this will be a most economical solution.

Keywords

Allocation, Multi-tenant, Virtualization, Scalability, Cloud Environment, Dynamic, Iaas.

References

[1]. Amazon Elastic Compute Cloud.http://aws.amazon.com/ec2/.
[2]. Abhishek Chandra, Weibo Gong, PrashantSheno.Dynamic Resource Allocation for Shared DataCentres Using Online Measurements 2003
[3]. J. Chase, D. Anderson, P. N. Thakar, and A. M. Vahdat.
[4]. Managing energy and server resources in hosting centers. InProc. SOSP, 2001.
[5]. X. Fan, W.-D.Weber, and L. A. Barroso. Power provisioningfor a warehouse-sized computer. In Proc. ISCA, 2007.
[6]. 2008the System S declarative stream processing engine
[7]. D. Gmach, J. Rolia, L. Cherkasova, and A. Kemper.
Capacitymanagement and demand prediction for next generation datacenters. In Proc. ICWS, 2007.
[8]. E. Kalyvianaki, T. Charalambous, and S. Hand. Selfadaptiveand self-configured CPU resource provisioning forvirtualized servers using Kalman filters. In Proc. ICAC,2009.
[9]. H. Lim, S. Babu, and J. Chase. Automated control for elasticstorage. In Proc. ICAC, 2010.
[10]. Xiaoyun Zhu, Zhikui Wang, SharadSinghal Utilitydriven workloadmanagement using nested control design. In Proc. AmericanControl Conference, 2006.
[11]. 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.
[12]. Z. Gong, X. Gu, and J. Wilkes. PRESS: PRedictive ElasticReSource Scaling for Cloud Systems.InProc. CNSM, 2010.
[13]. M. Armbrust, A. Fox, D. A. Patterson, N. Lanham,B. Trushkowsky, J. Trutna, and H. Oh. Scads:Scaleindependent storage for social computing applications.In Proc. CIDR, 2009.
[14]. ZhimingShen, SethuramanSubbiah, XiaohuiGu, John Wilkes, CloudScale: Elastic Resource Scaling for Multi-Tenant Cloud Systems 2011
[15]. ArchanaGanapathi†, Harumi Kuno§, UmeshwarDayal§, Janet L. Wiener,
[16]. Armando Fox†, Michael Jordan†, David Patterson