Defining a measure of cloud computing elasticity

Cloud computing has gathered great attention recently as a method for eliminating or at least reducing expensive setup and maintenance cost of computing resources. Cloud computing has many key characteristics such as reliability, multi-tenancy and rapid elasticity. However, these characteristics suffer from the lack of clear and quantitative measures. In this paper, we provide a preliminary work that can help in providing a set of benchmarks for a cloud computing performance. More specifically, we provide an approach for measuring the elasticity of a cloud. Elasticity of a cloud computing system refers to its ability to expand and contract overtime in response to users' demands. The work presented in this paper is inspired by the definition of elasticity that is used in physics. This definition is adopted to represent the basic features of a cloud computing environment and its parameters that are related to elasticity. Case study shows the adoption methodology and highlights some of the basic parameters affecting elasticity as measured by the proposed approach.

[1]  Alexandru Iosup,et al.  Performance Analysis of Cloud Computing Services for Many-Tasks Scientific Computing , 2011, IEEE Transactions on Parallel and Distributed Systems.

[2]  Rajkumar Buyya,et al.  NetworkCloudSim: Modelling Parallel Applications in Cloud Simulations , 2011, 2011 Fourth IEEE International Conference on Utility and Cloud Computing.

[3]  Rajkumar Buyya,et al.  Evaluating the cost-benefit of using cloud computing to extend the capacity of clusters , 2009, HPDC '09.

[4]  Jinesh Varia,et al.  Best Practices in Architecting Cloud Applications in the AWS Cloud , 2011 .

[5]  Randy H. Katz,et al.  Above the Clouds: A Berkeley View of Cloud Computing , 2009 .

[6]  Rajkumar Buyya,et al.  CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms , 2011, Softw. Pract. Exp..

[7]  Mladen A. Vouk,et al.  Cloud computing — Issues, research and implementations , 2008, ITI 2008 - 30th International Conference on Information Technology Interfaces.

[8]  Alexandru Iosup,et al.  On the Performance Variability of Production Cloud Services , 2011, 2011 11th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing.

[9]  L. Youseff,et al.  Toward a Unified Ontology of Cloud Computing , 2008, 2008 Grid Computing Environments Workshop.

[10]  Martin P. Robillard,et al.  Tracking Code Clones in Evolving Software , 2007, 29th International Conference on Software Engineering (ICSE'07).

[11]  Jorge-Arnulfo Quiané-Ruiz,et al.  Runtime measurements in the cloud , 2010, Proc. VLDB Endow..

[12]  Albert G. Greenberg,et al.  WebProphet: Automating Performance Prediction for Web Services , 2010, NSDI.

[13]  T. S. Eugene Ng,et al.  The Impact of Virtualization on Network Performance of Amazon EC2 Data Center , 2010, 2010 Proceedings IEEE INFOCOM.

[14]  Mladen A. Vouk,et al.  Cloud Computing – Issues, Research and Implementations , 2008, CIT 2008.

[15]  Dmitrii Zagorodnov,et al.  Eucalyptus : A Technical Report on an Elastic Utility Computing Archietcture Linking Your Programs to Useful Systems , 2008 .

[16]  Victor I. Chang,et al.  A Review of Cloud Business Models and Sustainability , 2010, 2010 IEEE 3rd International Conference on Cloud Computing.

[17]  Xiaowei Yang,et al.  CloudCmp: comparing public cloud providers , 2010, IMC '10.

[18]  Rajkumar Buyya,et al.  2011 Fourth IEEE International Conference on Utility and Cloud Computing SMICloud: A Framework for Comparing and Ranking Cloud Services , 2022 .

[19]  Harald C. Gall,et al.  Relation of Code Clones and Change Couplings , 2006, FASE.