Message Queuing Model for a Healthcare Hybrid
Cloud Computing Platform
Roxana MARCU, Iulian DANILA, Dan POPESCU, Oana CHENARU, Loretta ICHIM
University Politehnica Bucharest,
313 Splaiul Independentei, Bucharest, 060042, Bucharest, Romania. email@example.com; firstname.lastname@example.org; email@example.com
ABSTRACT: This paper sets forth a cloud platform for implementing a completely integrated system for healthcare that fulfils identified field specific requirements. To achieve a high level of performance at a low cost, the paper presents an analytical model for cloud computing scheduling. The proposed model takes into consideration classification based on message priority, separation of classification and processing from the message output servers, and random load of the incoming requests. Performance is measured in terms of the number of requests, waiting time, response time, and requests drop rate for each priority class defined. Experimental results indicate that proposed model is able to support a great number of arrival requests providing short response time related to priority classes.
KEYWORDS: Cloud computing, queuing model, performance analysis.
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CITE THIS PAPER AS:
Roxana MARCU, Iulian DANILA, Dan POPESCU, Oana CHENARU, Loretta ICHIM, Message Queuing Model for a Healthcare Hybrid Cloud Computing Platform, Studies in Informatics and Control, ISSN 1220-1766, vol. 26(1), pp. 95-104, 2017.
- Daskin, M. (2010). Service Science, John Wiley&Sons, Inc.
- Ellens, W., Zivkovnic, M., Akkerboom, J., Litjens, R. & Van Den Berg, H. (2012) Performance of cloud computing centres with multiple priority classes, Proceedings of the 5th IEEE International Conference on Cloud Computing (pp. 245-252).
- EMC (2014). Hybrid Cloud Powers Next-Generation Health IT at the Point-Of-Care, Whitepaper EMC Healthcare Solutions.
- Florea, I. & Sasu L. (2012). An Algorithm for Simulation of Waiting Systems with Different Types and Variable Number of Parallel Working Stations Each Having its Own Queue, Studies in Informatics and Control, 21 (3), 333-340.
- Guo, L., Yan, T., Zhao, S. & Jiang, C. (2014). Dynamic performance optimization for cloud computing using M/M/m queuing system, Hindawi Publishing Corporation Journal of Applied Mathematics.
- HASSAN, M. A., Kacem, I., Martin, S. & Osman, I. M. (2015). Genetic Algorithms for Job Scheduling in Cloud Computing, Studies in Informatics and Control, 24 (4), 387-400.
- Marcu, R., Popescu, D. & Danila I. (2014). Healthcare integration based on cloud computing. P.B. Sci. Bull., Series C, 77 (2), 31-42.
- Mohamed, E., Esedimy, E. I. & Rashad M. Z. (2014). Enhancing cloud computing scheduling based on queuing models, International Journal of Computer Applications (0975-8887), 85 (2).
- Nan, X., He, Y. & Guan, L. (2011). Optimal resource allocation for multimedia cloud in priority service scheme, Proceedings of the 13th IEEE International Workshop on Multimedia Signal Processing (pp. 1-6).
- Narman, H., Hossain, S. & Atiquzzaman, M. (2013). Dynamic Dedicated Servers Scheduling for Multi Priority Level Classes in Cloud Computing, IEEE Int. Conf. on Communications.
- Xiong, K. & Perros, H. (2009). Service Performance and Analysis in Cloud Computing, IEEE Congress on Services (SERVICES 2009).
- Wang, L., von Laszewski, G., Younge, A. et al. (2010). Cloud computing: a perspective study. New Generation Computing, 28 (2), 137–146.