Towards Dynamic Application Distribution Support for Performance Optimization in the Cloud

The Cloud computing paradigm emerged by establishing new resources provisioning and consumption models. Together with the improvement of resource management techniques, these models have contributed to an increase in the number of application developers that are strong supporters of partially or completely migrating their application to a highly scalable and pay-per-use infrastructure. In this paper we derive a set of functional and non-functional requirements and propose a process-based approach to support the optimal distribution of an application in the Cloud in order to handle fluctuating over time workloads. Using the TPC-H workload as the basis, and by means of empirical workload analysis and characterization, we evaluate the application persistence layer's performance under different deployment scenarios using generated workloads with particular behavior characteristics.

[1]  Frank Leymann,et al.  Web Services Platform Architecture: SOAP, WSDL, WS-Policy, WS-Addressing, WS-BPEL, WS-Reliable Messaging, and More , 2005 .

[2]  Jerome A. Rolia,et al.  Workload Analysis and Demand Prediction of Enterprise Data Center Applications , 2007, 2007 IEEE 10th International Symposium on Workload Characterization.

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

[4]  Mike P. Papazoglou,et al.  Blueprinting the Cloud , 2011, IEEE Internet Computing.

[5]  Schahram Dustdar,et al.  MADCAT: A Methodology for Architecture and Deployment of Cloud Application Topologies , 2014, 2014 IEEE 8th International Symposium on Service Oriented System Engineering.

[6]  Frank Leymann,et al.  Moving Applications to the Cloud: an Approach Based on Application Model Enrichment , 2011, Int. J. Cooperative Inf. Syst..

[7]  Frank Leymann,et al.  How to adapt applications for the Cloud environment , 2012, Computing.

[8]  José Luis Vázquez-Poletti,et al.  Provisioning data analytic workloads in a cloud , 2013, Future Gener. Comput. Syst..

[9]  Arshdeep Bahga,et al.  Synthetic Workload Generation for Cloud Computing Applications , 2011, J. Softw. Eng. Appl..

[10]  Christof Fetzer,et al.  VScaler: Autonomic Virtual Machine Scaling , 2013, 2013 IEEE Sixth International Conference on Cloud Computing.

[11]  A. Fox,et al.  Cloudstone : Multi-Platform , Multi-Language Benchmark and Measurement Tools for Web 2 . 0 , 2008 .

[12]  Wilhelm Hasselbring,et al.  Kieker: a framework for application performance monitoring and dynamic software analysis , 2012, ICPE '12.

[13]  David A. Patterson,et al.  Rain: A Workload Generation Toolkit for Cloud Computing Applications , 2010 .

[14]  Dimka Karastoyanova,et al.  Decision Support for the Migration of the Application Database Layer to the Cloud , 2013, 2013 IEEE 5th International Conference on Cloud Computing Technology and Science.

[15]  David J. DeWitt,et al.  Benchmarking Database Systems A Systematic Approach , 1983, VLDB.

[16]  Manish Marwah,et al.  Probabilistic performance modeling of virtualized resource allocation , 2010, ICAC '10.

[17]  Lizy K. John,et al.  Workload characterization: motivation, goals and methodology , 1998, Workload Characterization: Methodology and Case Studies. Based on the First Workshop on Workload Characterization.

[18]  Martin Fowler,et al.  Patterns of Enterprise Application Architecture , 2002 .

[19]  Frank Leymann,et al.  Portable Cloud Services Using TOSCA , 2012, IEEE Internet Computing.

[20]  Wei Chen,et al.  A Profit-Aware Virtual Machine Deployment Optimization Framework for Cloud Platform Providers , 2012, 2012 IEEE Fifth International Conference on Cloud Computing.

[21]  Patrick Martin,et al.  Executing Data-Intensive Workloads in a Cloud , 2012, 2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (ccgrid 2012).

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

[23]  Steffen Becker,et al.  Automatically improve software architecture models for performance, reliability, and cost using evolutionary algorithms , 2010, WOSP/SIPEW '10.