Composable cost estimation and monitoring for computational applications in cloud computing environments

Procedia Computer Science

Volumn 1, Issue 1, 2010, Pages 2175-2184

  Truong, Hong Linh ^a   Dustdar, Schahram ^a  

^a VIENNA UNIVERSITY OF TECHNOLOGY   (Austria)

Author keywords

Cloud computing; Cost analysis; Cost estimation; Cost monitoring; Scientific applications

Indexed keywords

CLOUD COMPUTING; COST ESTIMATING; COSTS; DISTRIBUTED COMPUTER SYSTEMS; ECONOMIC AND SOCIAL EFFECTS; NETWORK FUNCTION VIRTUALIZATION; SCHEDULING;

APPLICATION EXECUTION; CLOUD COMPUTING ENVIRONMENTS; COMPUTATIONAL APPLICATIONS; COST ANALYSIS; COST ESTIMATIONS; COST MONITORING; RESOURCE REQUIREMENTS; SCIENTIFIC APPLICATIONS;

COST BENEFIT ANALYSIS;

EID: 78650276567     PISSN: None     EISSN: 18770509     Source Type: Conference Proceeding    
DOI: 10.1016/j.procs.2010.04.243     Document Type: Conference Paper

References (14)

1. M. Armbrust, A. Fox, R. Grifth, A. D. Joseph, R. Katz, A. Konwinski, G. Lee, D. Patterson, A. Rabkin, I. Stoica, M. Zaharia, Above the clouds: A berkeley view of cloud computing, Tech. rep., University of California at Berkeley (February 2009). URL http://berkeleyclouds.blogspot.com/2009/02/above- clouds-released.html
2. I. T. Foster, Y. Zhao, I. Raicu, S. Lu, Cloud computing and grid computing 360-degree compared, CoRR abs/0901.0131.
3. R. Buyya, C. S. Yeo, S. Venugopal, Market-oriented cloud computing: Vision, hype, and reality for delivering it services as computing utilities, High Performance Computing and Communications, 10th IEEE International Conference on 0 (2008) 5-13. doi:http://doi.ieeecomputersociety.org/10.1109/ HPCC.2008.172.
4. H. L. Truong, P. Brunner, V. Nae, T. Fahringer, Dipas: A distributed performance analysis service for grid service-based workflows, Future Generation Comp. Syst. 25 (4) (2009) 385-398.
5. M. Geimer, S. Shende, A. D. Malony, F. Wolf, A generic and configurable source-code instrumentation component, in: G. Allen, J. Nabrzyski, E. Seidel, G. D. van Albada, J. Dongarra, P. M. A. Sloot (Eds.), ICCS (2), Vol. 5545 of Lecture Notes in Computer Science, Springer, 2009, pp. 696-705.
6. A. Knüpfer, D. Kranzlmüller, B. Mohr, W. E. Nagel, M09 - program analysis tools for massively parallel applications: how to achieve highest performance, in: SC, ACM Press, 2006, p. 223.
7. H. L. Truong, S. Dustdar, T. Fahringer, Performance metrics and ontologies for grid workflows, Future Generation Comp. Syst. 23 (6) (2007) 760-772.
8. E. Deelman, G. Singh, M. Livny, B. Berriman, J. Good, The cost of doing science on the cloud: the montage example, in: SC '08: Proceedings of the 2008 ACM/IEEE conference on Supercomputing, IEEE Press, Piscataway, NJ, USA, 2008, pp. 1-12. doi:http://doi.acm.org/10.1145/1413370.1413421.
9. C. Hoffa, G. Mehta, T. Freeman, E. Deelman, K. Keahey, B. Berriman, J. Good, On the use of cloud computing for scientific workflows, eScience, IEEE International Conference on 0 (2008) 640-645. doi:http://doi. ieeecomputersociety.org/10.1109/eScience.2008.167.
10. C. Evangelinos, C. Hill, Cloud computing for parallel scientific hpc applications: Feasibility of running coupled atmosphere-ocean climate models on amazon's ec2, in: The first workshop on Cloud Computing and its Applications (CCA'08), 2008, http://www.cca08.org/papers/Paper34-Chris-Hill.pdf.
11. M. R. Palankar, A. Iamnitchi, M. Ripeanu, S. Garfinkel, Amazon s3 for science grids: a viable solution?, in: DADC '08: Proceedings of the 2008 international workshop on Data-aware distributed computing, ACM, New York, NY, USA, 2008, pp. 55-64. doi:http://doi.acm.org/10.1145/1383519.1383526.
12. evapt instant saas metering, http://www.evapt.com/. Last access: 16 June 2009.
13. S. Pllana, T. Fahringer, Performance prophet: A performance modeling and prediction tool for parallel and distributed programs, in: ICPP Workshops, IEEE Computer Society, 2005, pp. 509-516.
14. M. Casas, R. M. Badia, J. Labarta, Prediction of behavior of mpi applications, in: CLUSTER, IEEE, 2008, pp. 242-251.