Design of a Performance Measurement Framework for Cloud Computing

DOI: 10.4236/jsea.2012.52011   PDF   HTML   XML   8,685 Downloads   15,534 Views   Citations


Cloud Computing is an emerging technology for processing and storing very large amounts of data. Sometimes anomalies and defects affect part of the cloud infrastructure, resulting in a performance degradation of the cloud. This paper proposes a performance measurement framework for Cloud Computing systems, which integrates software quality concepts from ISO 25010.

Share and Cite:

L. Bautista, A. Abran and A. April, "Design of a Performance Measurement Framework for Cloud Computing," Journal of Software Engineering and Applications, Vol. 5 No. 2, 2012, pp. 69-75. doi: 10.4236/jsea.2012.52011.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] H. Jin, S. Ibrahim, T. Bell, L. Qi, H. Cao, S. Wu and X. Shi, “Tools and Technologies for Building Clouds,” Cloud Computing: Principles, Systems and Applications, Computer Communications and Networks, Springer-Verlag, Berlin, 2010. doi:10.1007/978-1-84996-241-4_1
[2] G. Coulouris, J. Dollimore and T. Kindberg, “Distributed Systems Concepts and Design,” Addison-Wesley, 4th Edition, Pearson Education, Edinburgh, 2005.
[3] ISO/IEC Guide 99-12, “International Vocabulary of Metrology—Basic and General Concepts and Associated Terms, VIM,” International Organization for Standardization ISO/IEC, Geneva, 2007.
[4] ISO/IEC 15939, “Systems and Software Engineering—Measure,” International Organization for Standardization ement Process, Geneva, 2007.
[5] M. Burgess, H. Haugerud and S. Straumsnes, “Measuring System Normality,” ACM Transactions on Computer Systems, Vol. 20, No. 2, 2002, pp. 125-160. doi:10.1145/507052.507054
[6] A. Rao, R. Upadhyay, N. Shah, S. Arlekar, J. Raghothamma and S. Rao, “Cluster Performance Forecasting Using Predictive Modeling for Virtual Beowulf Clusters,” In: V. Garg, R. Wattenhofer and K. Kothapalli, Eds., ICDCN 2009, LNCS 5408, Springer-Verlag, Berlin, 2009, pp. 456-461.
[7] D. Smith, Q. Guan and S. Fu, “An Anomaly Detection Framework for Autonomic Management of Compute Cloud Systems,” IEEE 34th Annual IEEE Computer Software and Applications Conference Workshops, Seoul, 19-23 July 2010, pp. 376-381.
[8] J. Raj, “The Art of Computer Systems Performance Analysis: Techniques for Experimental Design, Measurement, Simulation, and Modeling,” Wiley-Interscience, New York, 1991.
[9] ISO/IEC 25010:2010(E), “Systems and Software Engineering—Systems and Software Product Quality Requirements and Evaluation (SQuaRE)—System and Software Quality Models,” International Organization for Standardization, Geneva, 2010.
[10] ISO/IEC 9126-1:2001(E), “Software Engineering—Pro- duct Quality—Part 1: Quality Model,” International Organization for Standardization, Geneva, 2001.
[11] ISO/IEC-19761, “Software Engineering—COSMIC v 3.0 —A Functional Size Measurement Method,” International Organization for Standardization, Geneva, 2003.
[12] A. Abran, “Software Metrics and Software Metrology,” John Wiley & Sons Interscience and IEEE-CS Press, New York, 2010. doi:10.1002/9780470606834
[13] K. Sarayreh, A. Abran and L. Santillo, “Measurement of Software Requirements Derived from System Reliability Requirements,” Workshop on Advances on Functional Size Measurement and Effort Estimation, 24th European Conference on Object Oriented Programming, Maribor, 20-22 June 2010.
[14] ECSS-E-ST-10C, “Space Engineering: System Engineering General Requirements,” European Cooperation for Space Standardization, Requirements & Standards Division, Noordwijk, 2009.

comments powered by Disqus

Copyright © 2020 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.