A field-based service management and discovery method in multiple clouds context

Abstract

In diverse and self-governed multiple clouds context, the service management and discovery are greatly challenged by the dynamic and evolving features of services. How to manage the features of cloud services and support accurate and efficient service discovery has become an open problem in the area of cloud computing. This paper proposes a field model of multiple cloud services and corresponding service discovery method to address the issue. Different from existing researches, our approach is inspired by Bohr atom model. We use the abstraction of energy level and jumping mechanism to describe services status and variations, and thereby to support the service demarcation and discovery. The contributions of this paper are threefold. First, we propose the abstraction of service energy level to represent the status of services, and service jumping mechanism to investigate the dynamic and evolving features as the variations and re-demarcation of cloud services according to their energy levels. Second, we present user acceptable service region to describe the services satisfying users’ requests and corresponding service discovery method, which can significantly decrease services search scope and improve the speed and precision of service discovery. Third, a series of algorithms are designed to implement the generation of field model, user acceptable service regions, service jumping mechanism, and user-oriented service discovery.We have conducted an extensive experiments on QWS dataset to validate and evaluate our proposed models and algorithms. The results show that field model can well support the representation of dynamic and evolving aspects of services in multiple clouds context and the algorithms can improve the accuracy and efficiency of service discovery.

Keywords

service field / service energy level / service jumping / service management / service discovery / multiple clouds

References

[1] Armbrust M. Above the clouds: a berkeley view of cloud computing. Sciences, 2009, 53(4): 50–58

[2] Foster I, Zhao Y, Raicu I, Lu S. Cloud computing and grid computing 360-degree compared. In: Proceedings of the Grid Computing Environments Workshop. 2008, 1–10

[3] Galante G, Bona L C E D. A survey on cloud computing elasticity. In: Proceedings of the 5th IEEE International Conference on Utility and Cloud Computing. 2012, 263–270

[4] Srirama S N, Iurii T, Viil J. Dynamic deployment and auto-scaling enterprise applications on the heterogeneous cloud. In: Proceedings of the 9th IEEE International Conference on Cloud Computing. 2016, 927–932

[5] Ferrer A J, Hernández F, Tordsson J, Elmroth E, Ali-Eldin A. OPTIMIS: a holistic approach to cloud service provisioning. Future Generation Computer Systems, 2012, 28(1): 66–77

[6] Petcu D. Consuming resources and services from multiple clouds. Journal of Grid Computing, 2014, 12(2): 321–345

[7] Zielinnski K, Szydlo T, Szymacha R, Kosinski J, Kosinska J. Adaptive SOA solution stack. IEEE Transactions on Services Computing, 2012, 5(2): 149–163

[8] Shi M, Liu J, Zhou D, Tang M, Cao B. WE-LDA: a word embeddings augmented LDA model forWeb services clustering. In: Proceedings of the IEEE International Conference on Web Services. 2017, 9–16

[9] Ngan L D, Kirchberg M, Kanagasabai R. Review of semantic Web service discovery methods. In: Proceedings of the 6th World Congress on Services. 2010, 176–177

[10] Ahmed M, Liu L, Hardy J, Yuan B. An efficient algorithm for partially matchedWeb services based on consumer’s QoS requirements. In: Proceedings of the 7th IEEE/ACMInternational Conference on Utility and Cloud Computing. 2014, 859–864

[11] Wang Y, He Q, Yang Y. QoS-aware service recommendation for multitenant SaaS on the cloud. In: Proceedings of the IEEE International Conference on Services Computing. 2015, 178–185

[12] Kumara B T G S, Paik I, Siriweera T, Koswatte K R. QoS aware service clustering to bootstrap the Web service selection. In: Proceedings of the IEEE International Conference on Services Computing. 2017, 233–240

[13] Sousa G, Rudametkin W, Duchien L. Automated setup of multi-cloud environments for microservices applications. In: Proceedings of the 9th IEEE International Conference on Cloud Computing. 2016, 327–334

[14] Kritikos K, Plexousakis D. Multi-cloud application design through cloud service composition. In: Proceedings of the 8th IEEE International Conference on Cloud Computing. 2015, 686–693

[15] Grozev N, Buyya R. Inter-cloud architectures and application brokering: taxonomy and survey. Software: Practice and Experience, 2014, 44(3): 369–390

[16] Liu G, Shen H. Minimum-cost cloud storage service across multiple cloud providers. In: Proceedings of the 36th IEEE International Conference on Distributed Computing Systems. 2016, 129–138

[17] Kritikos K, Plexousakis D. Multi-cloud application design through cloud service composition. In: Proceedings of the 8th IEEE International Conference on Cloud Computing. 2015, 686–693

[18] Elshater Y, Elgazzar K, Martin P. Godiscovery: Web service discovery made efficient. In: Proceedings of the IEEE International Conference on Web Services. 2015, 711–716

[19] Xie F, Liu J, Tang M, Cao B, Lyu S. Correlation search ofWeb services. In: Proceedings of Asia-Pacific Services Computing Conference. 2014, 107–114

[20] Liu L, Yao X, Qin L, Zhang M. Ontology-based service matching in cloud computing. In: Proceedings of the IEEE International Conference on Fuzzy Systems. 2014, 2544–2550

[21] Rodriguez J M, Zunino A, Mateos C, Segura F O, Rodriguez E. Improving REST service discovery with unsupervised learning techniques. In: Proceedings of the 9th International Conference on Complex, Intelligent, and Software Intensive Systems. 2015, 97–104

[22] Sha C, Wang K, Zhang K, Wang X, Zhou A. Diversifying top-k service retrieval. In: Proceedings of the IEEE International Conference on Services Computing. 2014, 227–234

[23] Gao W, Wu J. A novel framework for service set recommendation in mashup creation. In: Proceedings of the IEEE International Conference on Web Services. 2017, 65–72

[24] Yang W, Zhang C, Li J. An effective service discovery approach based on field theory and contribution degree in unstructured P2P networks. In: Proceedings of the 34th IEEE International Performance Computing and Communications Conference. 2015, 1–2

[25] Alfazi A, Sheng Q Z, Qin Y, Noor T H. Ontology-based automatic cloud service categorization for enhancing cloud service discovery. In: Proceedings of the 19th IEEE International Enterprise Distributed Object Computing Conference. 2015, 151–158

[26] Margaris D, Georgiadis P, Vassilakis C. A collaborative filtering algorithm with clustering for personalized Web service selection in business processes. In: Proceedings of the IEEE International Conference on Research Challenges in Information Science. 2015, 169–180

[27] Wang Y, He Q, Ye D, Yang Y. Service selection based on correlated QoS requirements. In: Proceedings of the IEEE International Conference on Services Computing. 2017, 241–248

[28] Ding S, Li Y, Wu D, Zhang Y, Yang S. Time-aware cloud service recommendation using similarity-enhanced collaborative filtering and ARIMA model. Decision Support Systems, 2018, 107: 103–115

[29] Ding S, Wang Z, Wu D, Olson D L. Utilizing customer satisfaction in ranking prediction for personalized cloud service selection. Decision Support Systems, 2017, 93: 1–10

[30] Ding S, Yang S, Zhang Y, Liang C, Xia C. Combining QoS prediction and customer satisfaction estimation to solve cloud service trustworthiness evaluation problems. Knowledge-Based Systems, 2014, 56: 216–225

[31] Torres R, Salas R. Self-adaptive fuzzy QoS-driven Web service discovery. In: Proceedings of the IEEE International Conference on Services Computing. 2011, 64–71

[32] Zhong Y, Fan Y, Huang K, Tan W, Zhang J. Time-aware service recommendation for mashup creation in an evolving service ecosystem. In: Proceedings of the IEEE International Conference on Web Services. 2014, 25–32

[33] Sun L, Wang S, Li J, Sun Q, Yang F. QoS uncertainty filtering for fast and reliable Web service selection. In: Proceedings of the IEEE International Conference on Web Services. 2014, 550–557

[34] Bohr N. On the constitution of atoms and molecules. The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science, 1913, 26(153): 476–502

[35] Kragh H. Niels Bohr and the Quantum Atom: the Bohr Model of Atomic Structure 1913–1925. Oxford: Oxford University Press, 2012

[36] Al-Masri E, Mahmoud Q H. QoS-based discovery and ranking of Web services. In: Proceedings of the 16th IEEE International Conference on Computer Communications and Networks. 2007, 529–534

[37] Arthur D, Vassilvitskii S. K-means++: the advantages of careful seeding. In: Proceedings of the 18th Annual ACM-SIAM Symposium on Discrete Algorithms. 2015, 1027–1035

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