Services Composition in Multi-cloud Environments using the Skyline Service Algorithm

Document Type : Original Article

Authors

Department of Computer Engineering, Yazd Branch, Islamic Azad University, Yazd, Iran

Abstract

The rapid growth of cloud environments has led to the expansion of resources that offer a variety of services. The opertions of the services are usually very simple and may not satisfy the  complex needs of the user, hence there is a need for a combination of these services that can fulfill the user's requirements. Most of the service composition methods in cloud environments assume that the involved services came from one cloud, and this is unrealistic because other clouds may provide more relevant services. The challenges in composition services distributed in multi-cloud environments include increased cost and a reduction in its speed due to the increasing number of services, providers, and clouds; so, in order to overcome these challenges, the number of providers and participating clouds must be reduced. This study used the Skyline service algorithm to compose services in multi-cloud environments, which examined all the clouds during the service composition process. The proposed method can provide an applicable composition service to the user with the lowest communication cost by considering the number of clouds and by using fewer providers. The Skyline algorithm involves two steps. In the first one, the best composition in a cloud environment is selected among all the possible providers by considering the number of providers and the communication time. In the second step, the Skyline algorithm is used to create all the possible compositions in a multi-cloud environment. Parameters such as fewer clouds and shorter communication times between the clouds are selected. The results show that the proposed method can find the composition with the least number of clouds, the lowest cost, and has the lowest calculation time. It can be said that the Skyline makes it possible to select a suitable composition of user-requested services in a multi-cloud environment.

Keywords


Curbera, F., Duftler, M., Khalaf, R., Nagy, W., Mukhi, N., & Weerawarana, S., “Unraveling the Web Services Web: an Introduction to SOAP, WSDL, and UDDI”, IEEE Internet Computing, Vol. 6, No. 2, (2002), 86-93. DOI: 10.1109/4236.991449
Guinard, D., Trifa, V., Karnouskos, S., Spiess, P., & Savio, D.,  “Interacting with the SOA-based Internet of Things: Discovery, Query, Selection, and on-demand Provisioning of Web Services”, IEEE Transactions on Services Computing, Vol. 3, No. 3, (2010), 223-235. DOI:10.1109/TSC.2010.3
Du, Y., Hu, H., Song, W., Ding, J., & Lü, J., “Efficient Computing Composite Service Skyline with QoS Correlations”, In 2015 IEEE International Conference on Services Computing, (2015), 41-48. DOI:10.1109/SCC.2015.16
Gabrel, V., Manouvrier, M., & Murat, C., “Web Services Composition: Complexity and Models”, Discrete AppliedMathematics, Vol. 196, (2015), 100-114. DOI: 10.1016/j.dam.2014.10.020
Cui, L., Kumara, S., & Lee, D., “Scenario Analysis of Web Service Composition based on Multi- Criteria Mathematical Goal Programming”, Service Science, Vol. 3, No. 4, (2011), 280-303. DOI: 10.1287/serv.3.4.280
Bypour, H., Farhadi, M., & Mortazavi, R., “An Efficient Secret Sharing-based Storage System for Cloud-based Internet of Things”, International Journal of Engineering, Vol. 32, No. 8, (2019), 1117-1125. DOI: 10.5829/ije.2019.32.08b.07
Jeyanthi, N., Shabeeb, H., Durai, M. S., & Thandeeswaran, R., “Reputation based Service for Cloud User Environment”, International Journal of Engineering, Transactions B: Applications, Vol.27, No. 8, (2014), 1179-1184. DOI: 10.5829/idosi.ije.2014.27.08b.03
Jula, A., Sundararajan, E., & Othman, Z., “Cloud Computing Service Composition: A Systematic Literature Review”, Expert Systems with Applications, Vol. 41, No. 8, (2014), 3809-3824. DOI: 10.1016/j.eswa.2013.12.017
Microsoft Communication & Media Industries, "Multi-Cloud Service Delivery end-to-end Management," Ref.architecture, 2013. https://cloudblogs.microsoft.c om/industry-blog/industry/uncategorized/multi-cloud- service-delivery-and-end-to-end-management-reference- architecture/
Venkat, M., 2016. Enterprise cloud strategy: Governance IBM. https://www.ibm.com/blogs/cloud-computing/2016/11/03/enterprise-governance-multi-cloud/
Yu, Q., & Bouguettaya, A., “Efficient Service Skyline Computation for Composite Service Selection”, IEEE Transactions on Knowledge and Data Engineering, Vol. 5, No. 4, (2013), 776-789. DOI: 10.1109/TKDE.2011.268
Belkasmi, D., Hadjali, A., & Azzoune, H., “On Fuzzy Approaches for Enlarging Skyline Query Results”,  Applied Soft Computing, Vol. 74, (2019), 51-65. DOI: 10.1016/j.asoc.2018.10.013
Elmi, S., & Min, J. K., “Spatial Skyline Queries over Incomplete Data for Smart Cities”, Journal of Systems Architecture, Vol. 90, (2018), 1-14. DOI: 10.1016/j.sysarc.2018.08.005
Lim, J., Li, H., Bok, K., & Yoo, J., “A Continuous Reverse Skyline Query Processing Method in Moving Objects Environments”, Data & Knowledge Engineering, Vol. 104, (2016), 45-58. DOI: 10.1016/j.datak.2015.05.003
Yang, Z., Li, K., Zhou, X., Mei, J., & Gao, Y., “Top k Probabilistic Skyline Queries on Uncertain Data”,  Neurocomputing, Vol. 317, (2018), 1-14. DOI: 10.1016/j.neucom.2018.03.052
Zou, G., Chen, Y., Yang, Y., Huang, R., & Xu,Y., “AI Planning and Combinatorial Optimization for Web Service Composition in Cloud Computing”, In Proccedding of the International Conference on Cloud Computing and Virtualization, (2010), 1-8. DOI: 10.5176/978-981-08-5837-7_166
Gutierrez-Garcia, J. O., & Sim, K. M., “Agent-based Cloud Service Composition”, Applied Intelligence, Vol. 38, No. 3, (2013), 436-464. DOI: 10.1007/s10489-012-0380-x
Jatoth, C., Gangadharan, G.R., & Buyya, R., “Optimal Fitness Aware Cloud Service Composition using an Adaptive Genotypes Evolution based Genetic Algorithm”,  Future Generation Computer Systems, Vol. 94, (2019), 185-198. DOI: 10.1016/j.future.2018.11.022
Jatoth, C., Gangadharan, G. R., & Fiore, U., “Optimal Fitness Aware Cloud Service Composition using Modified Invasive Weed Optimization”, Swarm and Evolutionary Computation, Vol. 44, (2019), 1073-1091. DOI: 10.1016/j.swevo.2018.11.001
Gavvala, S. K., Jatoth, C., Gangadharan, G. R., & Buyya, R., “QoS-Aware Cloud Service Composition using Eagle Strategy”, Future Generation Computer Systems, Vol. 90, (2019), 273-290. DOI: 10.1016/j.future.2018.07.062
Yu, Q., Chen, L., & Li, B., “Ant Colony Optimization Applied to Web Service Compositions in Cloud Computing”, Computers & Electrical Engineering, Vol. 41, (2015), 18-27. DOI: 10.1016/j.compeleceng.2014.12.004
Kurdi, H., Al-Anazi, A., Campbell, C., & Al Faries, A., “A Combinatorial Optimization Algorithm for Multiple Cloud Service Composition”, Computers & Electrical Engineering, Vol. 42, (2015), 107-113. DOI: 10.1016/j.compeleceng.2014.11.002
Mezni, H., & Sellami, M., “Multi-Cloud Service Composition using Formal Concept Analysis”, Journal of Systems and Software, Vol. 134, (2017), 138-152. DOI: 10.1016/j.jss.2017.08.016
Mezni, H., & Abdeljaoued, T., “A Cloud Services Recommendation System based on Fuzzy Formal Concept Analysis”, Data & Knowledge Engineering, Vol. 116, (2018), 100-123. DOI: 10.1016/j.datak.2018.05.008
Wu, J., Chen, L., & Liang, T., “Selecting Dynamic Skyline Services for QoS-based Service Composition”, Applied Mathematics & Information Sciences, Vol. 8, No. 5, (2014), 2579. DOI: DOI: 10.1145/1772690.1772693
Zhang, F., Hwang, K., Khan, S., & Malluhi, Q., “Skyline Discovery and Composition of Inter-Cloud Mashup Services”, IEEE Transactions on Services Computing, Vol. 9, No. 1, (2016), 72-83. DOI: 10.1109/TSC.2015.2449302
Zhang, J., Jiang, X., Ku, W. S., & Qin, X., “Efficient Parallel Skyline Evaluation using Mapreduce”, IEEE Transactions on Parallel and Distributed Systems, Vol. 27, No. 7, (2016), 1996-2009. DOI: 10.1109/TPDS.2015.2472016
Liu, Y., Yang, R., & Zhang, S., “Service Selection Method based on Skyline in Cloud Environment”, International Journal of Performability Engineering, Vol. 13, No. 7, (2017). DOI: 10.23940/ijpe.17.07.p5.10391047
Moradi, M., & Emadi, S., “Reducing the Calculations of Quality-Aware Web Services Composition Based on Parallel Skyline Service”, International Journal of Advanced Computer Science and Applications, Vol. 7, No. 7, (2016). DOI: 10.14569/IJACSA.2016.070744
Borzsony, S., Kossmann, D., & Stocker, K., “The skyline Operator”, In Proceedings 17th IEEE International Conference on Data Engineering, (2001), 421- 430. DOI: 10.1109/ICDE.2001.914855
Papadias, D., Tao, Y., Fu, G., & Seeger, B., “Progressive skyline Computation in Database Systems”, ACM Transactions on Database Systems, Vol. 30, No. 1, (2005), 41-82. DOI: 10.1145/1061318.1061320
Wang, Y., Song, Y., & Liang, M., “A Skyline-based Efficient Web Service Selection Method Supporting Frequent Requests”, In 2016 IEEE 20th International Conference on Computer Supported Cooperative Work in Design (CSCWD), (2016), 328-333. DOI: 10.1109/CSCWD.2016.7566009
Fariss, M., Asaidi, H., & Bellouki, M., “Comparative Study of Skyline Algorithms for Selecting Web Services based on QoS”,  Procedia Computer Science 127, (2018), 408-415. DOI: 10.1016/j.procs.2018.01.138
Alrifai, M., Skoutas, D., & Risse, T., “Selecting Skyline Services for QoS-based Web Service Composition”, In Proceedings of the 19th International Conference on World Wide Web, (2010), 11-20. DOI: 10.1145/1772690.1772693
Benouaret, K., Benslimane, D., & Hadjali, A., “Ws-Sky: An Efficient and Flexible Framework for QoS-aware Web Service Selection”, In IEEE Ninth International Conference on Services Computing, (2012), 146-153. DOI:10.1109/SCC.2012.83
Fekih, H., Mtibaa, S., & Bouamama, S., “Local-Consistency Web Services Composition Approach based on Harmony Search”, Procedia Computer Science 112, (2017), 1102-1111. DOI: 10.1016/j.procs.2017.08.135