QoS based Resource Allocation and Service Selection in the Cloud

Rima Grati, Khouloud Boukadi, Hanêne Ben-Abdallah


Web service composition builds a new value-added web service using existing web services. A web service may have many implementations, all of which have the same functionality, but may have different Quality of Service (QoS) values. Hence, a challenging issue of web service composition is how to meet QoS and to fulfil cloud customers’ expectations and preferences in the inherently dynamic environment of the Cloud. Addressing the QoS based web service selection and resource allocation is the focus of this paper. This challenge is a multi-objective optimization problem. To tackle this complex problem, we propose a new Penalty Genetic Algorithm (PGA) to help a Cloud provider quickly determine a set of services that compose the workflow of the composite web service. The proposed approach aims to, at the one hand, meet QoS constraints prioritized by the Cloud customer and, at the other hand, respect the resource constraints of the Cloud provider. To the best of our knowledge, this is the first attempt to handle the problem of the optimal selection of web services while taking into account the resource allocation in order to guarantee the QoS imposed by the Cloud customer and to maximize the profit of the Cloud provider. The experimental results of Penalty Genetic Algorithm show that it outperforms the Integer Programming method when the number of web services and the number of resources are large.


  1. Espadas, J.; Molina, A.; Jimeneza, G.; Molinab, M.; Ramíreza, R. A tenant-based resource allocation model for scaling Software-as-a-Service applications over cloud computing infrastructures. Future Gener Comput Syst. 2013;29(1):273-286.
  2. Qiang, D. Resource allocation in buffered crossbar switches for supporting network virtualization. High Performance Switching and Routing (HPSR), 2010 International Conference on; 2010. p. 147-152.
  3. Yusoh, M.; Izzah, Z.; Maolin, T. Clustering composite SaaS components in Cloud computing using a Grouping Genetic Algorithm. Evolutionary Computation (CEC), 2012 IEEE Congress on; 2012. p. 1-8.
  4. Canfora, G.; Penta, M.D.; Esposito, R.; Villani, M.L. An approach for QoS-aware service composition based on genetic algorithms. Proceedings of the 7th annual conference on Genetic and evolutionary computation. Washington DC, USA: ACM; 2005. p. 1069-1075.
  5. Li, W.; Zhong, Y.; Wang, X.; Cao, Y. Resource virtualization and service selection in cloud logistics. J Netw Comput Appl. 2013;36(6):1696-1704.
  6. Wada, H.; Suzuki, J.; Yamano, Y.; Oba, K. E3: A Multiobjective Optimization Framework for SLAAware Service Composition. IEEE Transactions on Services Computing. 2012;5(3):358-372.
  7. Wang, S.; Zibin, Z.; Qibo, S.; Hua, Z.; Fangchun, Y. Cloud model for service selection. Computer Communications Workshops (INFOCOM WKSHPS), 2011 IEEE Conference on; 2011. p. 666-671.
  8. Linlin, W.; Garg, S.K.; Buyya, R. SLA-Based Resource Allocation for Software as a Service Provider (SaaS) in Cloud Computing Environments. Cluster, Cloud and Grid Computing (CCGrid), 2011 11th IEEE/ACM International Symposium on; 2011. p. 195-204.
  9. Zhu, Q.; Agrawal, G. Resource provisioning with budget constraints for adaptive applications in cloud environments. Vol. Vol 5, IEEE Transactions on Services Computing; 2012. p. 497-511.
  10. Papagianni, C.; Leivadeas, A.; Papavassiliou, S.; Maglaris, V.; Cervello-Pastor, C.; Monje, A. On the optimal allocation of virtual resources in cloud computing networks. Computers, IEEE Transactions on. 2013;62(6):1060-1071.
  11. Karakoc, E.; Kardas, K.; Senkul, P. A Workflow-Based Web Service Composition System. Web Intelligence and Intelligent Agent Technology Workshops, 2006 WI-IAT 2006 Workshops 2006 IEEE/WIC/ACM International Conference on; 2006. p. 113-116.
  12. BangYu, W.; Chi-Hung, C.; Zhe, C. Resource Allocation Based On Workflow For Enhancing the Performance of Composite Service. Services Computing, 2007 SCC 2007 IEEE International Conference on; 2007. p. 552- 559.
  13. Coello, C.A.C. Constraint-handling techniques used with evolutionary algorithms. Proceedings of the 12th annual conference companion on Genetic and evolutionary computation. Portland, Oregon, USA: ACM; 2010. p. 2603-2624.
  14. Cardoso, J. Quality of service and semantic composition of workflows [USA]: University of Georgia, Athens; 2002.
  15. Jaeger, M. Optimising Quality of Service for the composition of electronic services [Berlin]: Technischte Universit; 2006.
  16. Kuri Morales; C.V. Quezada. A universal eclectic genetic algorithm for constrained optimization. 6th European Congress on Intelligent Techniques and Soft Computing, EUFIT'98. Verlag Mainz, Aachen, Germany; 1998. p. 518-522.

Paper Citation

in Harvard Style

Grati R., Boukadi K. and Ben-Abdallah H. (2014). QoS based Resource Allocation and Service Selection in the Cloud . In Proceedings of the 11th International Conference on e-Business - Volume 1: ICE-B, (ICETE 2014) ISBN 978-989-758-043-7, pages 249-256. DOI: 10.5220/0005059602490256

in Bibtex Style

author={Rima Grati and Khouloud Boukadi and Hanêne Ben-Abdallah},
title={QoS based Resource Allocation and Service Selection in the Cloud},
booktitle={Proceedings of the 11th International Conference on e-Business - Volume 1: ICE-B, (ICETE 2014)},

in EndNote Style

JO - Proceedings of the 11th International Conference on e-Business - Volume 1: ICE-B, (ICETE 2014)
TI - QoS based Resource Allocation and Service Selection in the Cloud
SN - 978-989-758-043-7
AU - Grati R.
AU - Boukadi K.
AU - Ben-Abdallah H.
PY - 2014
SP - 249
EP - 256
DO - 10.5220/0005059602490256