Journal of Scheduling

, Volume 20, Issue 1, pp 103–113 | Cite as

Optimization-based resource allocation for software as a service application in cloud computing

  • Chunlin LiEmail author
  • Yun Chang Liu
  • Xin Yan


Software as a service (SaaS) is a software that is developed and hosted by the SaaS vendor. SaaS cloud provides software as services to the users through the internet. To provide good quality of service for the user, the SaaS relies on the resources leased from infrastructure as a service cloud providers. As the SaaS services rapidly expand their application scopes, it is important to optimize resource allocation in SaaS cloud. The paper presents optimization-based resource allocation approach for software as a service application in cloud. The paper uses optimization decomposition approach to solve cloud resource allocation for satisfying the cloud user’s needs and the profits of the cloud providers. The paper also proposes a SaaS cloud resource allocation algorithm. The experiments are designed to compare the performance of the proposed algorithm with other two related algorithms.


Cloud computing Software as a service (SaaS) Resource allocation Quality of service (QoS) 



The authors thank the editors and the anonymous reviewers for their helpful comments and suggestions. The work was supported by the National Natural Science Foundation (NSF) under grants (Nos. 61472294, 61672397), Key Natural Science Foundation of Hubei Province (No. 2014CFA050), Open Project Program of Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education (No. 201602), Applied Basic Research Project of WuHan (No. 2015010101010021), Program for the High-end Talents of Hubei Province. Any opinions, findings, and conclusions are those of the authors and do not necessarily reflect the views of the above agencies.


  1. Abhishek, V., Zenia, P. G., Noella, F., & Flavin, C. (2015). Cloud computing using OCRP and virtual machines for dynamic allocation of resources. Technologies for sustainable development (ICTSD), 2015 International Conference on (pp 1–5)Google Scholar
  2. Abdul, H., Alireza, K., & Rajiv, R. (2014). A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing, 98(7), 1–24.Google Scholar
  3. Abhinandan, S. P., & Shrisha, R. (2014). A mechanism design approach to resource procurement in cloud computing. IEEE Transactions on Computers, 63(1), 17–30.CrossRefGoogle Scholar
  4. Al-Ayyoub, M., Jararweh, Y., Daraghmeh, M., & Althebyan, Q. (2015). Multi-agent based dynamic resource provisioning and monitoring for cloud computing systems infrastructure. Cluster Computing, 18(2), 919–932.CrossRefGoogle Scholar
  5. Armbrust, M., Fox, A., Griffith, R., Joseph, A. D., Katz, R., Konwinski, A., et al. (2010). A view of cloud computing. Communications of the ACM, 53, 50–58.CrossRefGoogle Scholar
  6. Bo, A., Lesser, V., Irwin, D., et al. (2010). Automated negotiation with decommitment for dynamic resource allocation in cloud computing. In Proceedings of the 9th international conference on autonomous agents and multiagent systems, AAMAS ’10 (Vol. 1, pp. 981–988)Google Scholar
  7. Chaisiri, S., Lee, B.-S., & Niyato, D. (2012). Optimization of resource provisioning cost in cloud computing. IEEE Transactions on Services Computing, 5(2), 164–177.CrossRefGoogle Scholar
  8. Cheng, Y., Chen, Y., Wei, R., & Luo, H. (2015). Development of a construction quality supervision collaboration system based on a SaaS private cloud. Journal of Intelligent & Robotic Systems, 79(3), 613–627.CrossRefGoogle Scholar
  9. Chrysa, P., Leivadeas, A., Papavassiliou, S., Maglaris, V., Cervello’-Pastor, C., & Monje, A. (2013). On the optimal allocation of virtual resources in cloud computing networks. IEEE Transactions on Computers, 62(6), 1060–1071.Google Scholar
  10. CloudSim. (2014).
  11. Erdil, D. C. (2012). Simulating peer-to-peer cloud resource scheduling. Peer-to-Peer Networking and Applications, 5(3), 219–230.CrossRefGoogle Scholar
  12. Florin, P., Dobre, C., Cristea, V., Bessis, N., Xhafa, F., & Barolli, L. (2015). Deadline scheduling for aperiodic tasks in inter-Cloud environments a new approach to resource management. The Journal of Supercomputing, 71(5), 1754–1765.CrossRefGoogle Scholar
  13. Hassan, M. M., Hossain, M. S., Sarkar, A. M. J., & Huh, E.-N. (2014). Cooperative game-based distributed resource allocation in horizontal dynamic cloud federation platform. Information Systems Frontiers, 16(4), 523–542.CrossRefGoogle Scholar
  14. Kang, Z., & Hongbing, W. (2013). A novel approach to allocate cloud resource with different performance traits. In Services Computing (SCC), 2013 IEEE International Conference on (pp 128–135)Google Scholar
  15. Kim, H.-Woo., Park, J. H., & Jeong, Y.-S. (2016). Human-centric storage resource mechanism for big data on cloud service architecture. The Journal of Supercomputing, 72(7), 2437–2452.Google Scholar
  16. Lee, H. M., Jeong, Y.-S., & Jang, H. J. (2014). Performance analysis based resource allocation for green cloud computing. The Journal of Supercomputing, 69(3), 1013–1026.Google Scholar
  17. Lien, D., Bert, V., Pieter, S., Filip, D. T., Bart, D., & Piet D. (2012). Efficient resource management for virtual desktop cloud computing. The Journal of Supercomputing, 62(2), 741–767.Google Scholar
  18. Li, C., & Li, L. (2013). Efficient resource allocation for optimizing objectives of cloud user, IaaS provider and SaaS provider in cloud environment. Journal of Supercomputing, 65(2), 866–885.CrossRefGoogle Scholar
  19. Li, C., & Li, L. (2014). Phased scheduling for resource-constrained mobile devices in mobile cloud computing. Wireless Personal Communications, 77(4), 2817–2837. (Springer-Verlag).CrossRefGoogle Scholar
  20. Pattanaik, P. A,, Roy, S., & Pattnaik, P. K. (2015). Performance study of some dynamic load balancing algorithms in cloud computing. In Signal processing and integrated networks (SPIN), 2015 2nd International Conference on (pp 619–624)Google Scholar
  21. Son, S., Jung, G., & Jun, S. C. (2013). An SLA-based cloud computing that facilitates resource allocation in the distributed data centers of a cloud provider. The Journal of Supercomputing, 64(2), 606–637.CrossRefGoogle Scholar
  22. Spotcloud. (2014). Cloud capacity clearing house: spot market: Home.
  23. Steffen, B., & Matthias, T., (2012). Towards model-driven evolution of performance critical business information systems to cloud computing architectures. Softwaretechnik-Trends, 32(2), 7–8.Google Scholar
  24. Sukhpal, S., & Inderveer, C. (2015). QRSF QoS-aware resource scheduling framework in cloud computing. The Journal of Supercomputing, 71(1), 241–292.CrossRefGoogle Scholar
  25. Victor, I. M., Calin, S., & Dana, P. (2014). Multi-cloud resource management cloud service interfacing. Journal of Cloud Computing, 3(1), 1–23.CrossRefGoogle Scholar
  26. Wang, E. D., Wu N., & Li X. (2013). QoS-oriented monitoring model of cloud computing resources availability. In 2013 Fifth international conference on computational and information sciences (ICCIS) (pp. 1537–1540)Google Scholar
  27. Wei, Y., & Brian, B. M. (2016). Proactive virtualized resource management for service workflows in the cloud. Computing, 98(5), 523–538.CrossRefGoogle Scholar
  28. Wu, L., Garg, S. K., & Buyya, R, (2011). SLA-based resource allocation for a software as a service provider in cloud computing environments. In Proceedings of the 11th IEEE/ACM international symposium on cluster computing and the grid (CCGrid 2011), May 23–26, Los AngelesGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Department of Computer ScienceWuhan University of TechnologyWuhanPeople’s Republic of China
  2. 2.Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of EducationNanjing University of Science and TechnologyNanjingPeople’s Republic of China

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