A Methodology for Economic Evaluation of Cloud-Based Web Applications

Cloud technology is an attractive infrastructure solution to optimize the scalability and performance of web applications. The workload of these applications typically fluctuates between peak and valley loads and sometimes in an unpredictable way. Cloud systems can easily deal with this fluctuation because they provide customers with an almost unlimited on-demand infrastructure capacity using a pay-per-use model, which enables internet-based companies to pay for the actual consumption instead of peak capacity. In this paradigm, this paper links the business model of an internet-based company to the performance evaluation of the infrastructure. To this end, the paper develops a new methodology for assessing the costs and benefits of implementing web-based applications in the cloud. Traditional performance models and indexes related to usage of the main system resources (such as processor, memory, storage, and bandwidth) have been reformulated to include new metrics (such as customer losses and service costs) that are useful for business managers. Additionally, the proposed methodology has been illustrated with a case study of a typical e-commerce scenario. Experimental results show that the proposed metrics enable internet-based companies to estimate the cost of adopting a particular cloud configuration more accurately in terms of the infrastructure cost and the cost of losing customers due to performance degradation. Consequently, the methodology can be a useful tool to assess the feasibility of business plans.

[1]  D. Teece Business Models, Business Strategy and Innovation , 2010 .

[2]  Yu-Ju Hong,et al.  Dynamic server provisioning to minimize cost in an IaaS cloud , 2011, PERV.

[3]  Ana Pont,et al.  Providing TCP-W with web user dynamic behavior , 2012, CLEI Electron. J..

[4]  Yi Peng,et al.  Evaluation of Classification Algorithms Using MCDM and Rank Correlation , 2012, Int. J. Inf. Technol. Decis. Mak..

[5]  Diwakar Krishnamurthy,et al.  A model-based approach for testing the performance of web applications , 2006, SOQUA '06.

[6]  Gabriel Antoniu,et al.  A performance evaluation of Azure and Nimbus clouds for scientific applications , 2012, CloudCP '12.

[7]  D.A. Menasce,et al.  Scaling for e-business , 2000, Proceedings 8th International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems (Cat. No.PR00728).

[8]  S. Khaddaj,et al.  Quality Measurement for Cloud Based E-commerce Applications , 2012, 2012 11th International Symposium on Distributed Computing and Applications to Business, Engineering & Science.

[9]  Ana Pont,et al.  Analyzing web server performance under dynamic user workloads , 2013, Comput. Commun..

[10]  Ana Pont,et al.  Dweb model: Representing Web 2.0 dynamism , 2009, Comput. Commun..

[11]  Virgílio A. F. Almeida,et al.  Hierarchical Characterization and Generation of Blogosphere Workloads , 2008 .

[12]  Sally Floyd,et al.  Difficulties in simulating the internet , 2001, TNET.

[13]  Verena Kantere,et al.  Optimal Service Pricing for a Cloud Cache , 2011, IEEE Transactions on Knowledge and Data Engineering.

[14]  Ana Pont,et al.  An approach for economic evaluation of cloud-based applications , 2014, 2014 IEEE 3rd International Conference on Cloud Networking (CloudNet).

[15]  Prasant Mohapatra,et al.  Characterization of E-Commerce Traffic , 2003, Electron. Commer. Res..

[16]  Raúl Peña Ortiz,et al.  Providing TPC-W with web user dynamic behavior , 2012 .

[17]  Dennis F. Galletta,et al.  Web Site Delays: How Tolerant are Users? , 2004, J. Assoc. Inf. Syst..

[18]  Virgílio A. F. Almeida,et al.  Characterizing user behavior in online social networks , 2009, IMC '09.

[19]  Jordi Torres,et al.  A methodology for the evaluation of high response time on E-commerce users and sales , 2014, Inf. Syst. Frontiers.

[20]  Ana Pont,et al.  Generating realistic workload for web performance studies , 2015 .

[21]  Eduard Ayguadé,et al.  Non-intrusive Estimation of QoS Degradation Impact on E-Commerce User Satisfaction , 2011, 2011 IEEE 10th International Symposium on Network Computing and Applications.

[22]  Drasko Tomic,et al.  Economics of the cloud computing , 2011, 2011 Proceedings of the 34th International Convention MIPRO.

[23]  John P. Conley,et al.  The Economics of Cloud Computing , 2011 .

[24]  Daniel A. Menascé,et al.  Scaling for E-Business: Technologies, Models, Performance, and Capacity Planning , 2000 .

[25]  Ck Cheng,et al.  The Age of Big Data , 2015 .

[26]  María S. Pérez-Hernández,et al.  Consistency in the Cloud: When Money Does Matter! , 2013, 2013 13th IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing.

[27]  P. Mell,et al.  The NIST Definition of Cloud Computing , 2011 .

[28]  Yi Peng,et al.  Evaluation of clustering algorithms for financial risk analysis using MCDM methods , 2014, Inf. Sci..

[29]  Baochun Li,et al.  Towards Optimal Capacity Segmentation with Hybrid Cloud Pricing , 2012, 2012 IEEE 32nd International Conference on Distributed Computing Systems.

[30]  Ian Lumb,et al.  A Taxonomy and Survey of Cloud Computing Systems , 2009, 2009 Fifth International Joint Conference on INC, IMS and IDC.

[31]  Diwakar Krishnamurthy,et al.  Performance Testing Web Applications on the Cloud , 2014, 2014 IEEE Seventh International Conference on Software Testing, Verification and Validation Workshops.

[32]  Federico Etro,et al.  The Economic Impact of Cloud Computing on Business Creation, Employment and Output in Europe. An application of the Endogenous Market Structures Approach to a GPT innovation , 2009 .

[33]  Ana Pont,et al.  Surfing the Web Using Browser Interface Facilities: A Performance Evaluation Approach , 2015, J. Web Eng..

[34]  Huan Liu,et al.  Client-side load balancer using cloud , 2010, SAC '10.

[35]  Stephen A. Jarvis,et al.  Predictive and Dynamic Resource Allocation for Enterprise Applications , 2010, 2010 10th IEEE International Conference on Computer and Information Technology.

[36]  Randy H. Katz,et al.  A view of cloud computing , 2010, CACM.