Evaluating Cloud Platform Architecture with the CARE Framework

There is an emergence of Cloud application platforms such as Microsoft’s Azure, Google’s App Engine and Amazon’s EC2/SimpleDB/S3. Startups and Enterprise alike, lured by the promise of ‘infinite scalability’, ‘ease of development’, ‘low infrastructure setup cost’ are increasingly using these Cloud service building blocks to develop and deploy their web based applications. However, the precise nature of these Cloud platforms and the resultant Cloud application runtime behavior is still largely an unknown. Given the black box nature of these platforms, and the novel programming and data models of Cloud, there is a dearth of tools and techniques for enabling the rigorously evaluation of Cloud platforms at runtime. This paper introduces the CARE (Cloud Architecture Runtime Evaluation) approach, a framework for evaluating Cloud application development and runtime platforms. CARE implements a unified interface with WSDL and REST in order to evaluate different Cloud platforms for Cloud application hosting servers and Cloud databases. With the unified interface, we are able to perform selective high stress and low stress evaluations corresponding to desired test scenarios. Result shows the effectiveness of CARE in the evaluation of Cloud variations in terms of scalability, availability and responsiveness, across both compute and storage capabilities. Thus placing CARE as an important tool in the path of Cloud computing research.

[1]  Carsten Binnig,et al.  How is the weather tomorrow?: towards a benchmark for the cloud , 2009, DBTest '09.

[2]  Ravi Iyer,et al.  Modeling virtual machine performance: challenges and approaches , 2010, PERV.

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

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

[5]  Rajkumar Buyya,et al.  Article in Press Future Generation Computer Systems ( ) – Future Generation Computer Systems Cloud Computing and Emerging It Platforms: Vision, Hype, and Reality for Delivering Computing as the 5th Utility , 2022 .

[6]  Constantinos Evangelinos,et al.  Cloud Computing for parallel Scientific HPC Applications: Feasibility of Running Coupled Atmosphere- , 2008 .

[7]  Ian Gorton,et al.  Accelerating COTS Middleware Acquisition: The i-Mate Process , 2003, IEEE Softw..

[8]  Tim Kraska,et al.  An evaluation of alternative architectures for transaction processing in the cloud , 2010, SIGMOD Conference.

[9]  Wilson C. Hsieh,et al.  Bigtable: A Distributed Storage System for Structured Data , 2006, TOCS.

[10]  Anna Liu.  DeBOT-an approach for constructing high performance, scalable distributed object systems , 2000, Proceedings of the 2000 International Conference on Software Engineering. ICSE 2000 the New Millennium.

[11]  Marty Humphrey,et al.  A quantitative analysis of high performance computing with Amazon's EC2 infrastructure: The death of the local cluster? , 2009, 2009 10th IEEE/ACM International Conference on Grid Computing.

[12]  Miron Livny,et al.  The cost of doing science on the cloud: The Montage example , 2008, 2008 SC - International Conference for High Performance Computing, Networking, Storage and Analysis.

[13]  Paul Brebner,et al.  Rigorous Evaluation of COTS Middleware Technology , 2003, Computer.

[14]  Chris Rose,et al.  A Break in the Clouds: Towards a Cloud Definition , 2011 .

[15]  G. Bruce Berriman,et al.  On the Use of Cloud Computing for Scientific Workflows , 2008, 2008 IEEE Fourth International Conference on eScience.