Abstract
Search and selection of simulation model is an important process of building simulation application of complex system based on model composition in cloud architecture environment. This paper aims to solve the problem of lacking model correlation search and quality of service (QoS) weighted selection. The knowledge graph is used to describe the simulation models and their correlations. According to the model attributes (such as model name, domain, type, time scale, model granularity, etc.) and the model correlation (such as equipment model carrying relationship, etc.) set by users, the initial set of simulation models satisfying the requirements is found based on semantic search. Then, an optimization selection mechanism based on QoS is proposed to support users in customizing the weights of the QoS indices. The optimally ordered model candidate set is provided for selecting according to the weighting comparison of QoS indices. The experimental results show that the proposed method based on semantic search can support the effective selection of simulation models in cloud environment and the composite modeling of complex systems.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Calheiros, R.N., Ranjan, R., Beloglazov, A., Rose, C.A.F.D., Buyya, R.: CloudSim: a toolkit for modelling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Softw. Pract. Exp. 41(1), 23–50 (2011)
Sotiriadis, S., Bessis, N., Antonopoulos, N., et al.: SimIC: designing a new inter-cloud simulation platform for integrating large-scale resource management. In: IEEE International Conference on Advanced Information Networking and Applications (2013)
Taylor, S.J.E., et al.: Grand challenges for modelling and simulation: simulation everywhere—from cyber infrastructure to clouds to citizens. Simulation 91(7), 648–665 (2015)
Moghaddam, M., Davis, J.G.: Service Selection in Web service Composition: A Comparative Review of Existing Approaches. Springer, New York (2014). 10.1007/978-1-4614-7518-7_13
Christensen, E., et al.: Web services description language (WSDL). In: Encyclopedia of Social Network Analysis and Mining (2003
Moreau (Canon), J.: Web services Description Language (WSDL) Version 1.2: Bindings (2003)
Yao, Y., Liu, G.: High-performance simulation computer for large-scale system-of-systems simulation. J. Syst. Simul. 23(8), 1617–1623 (2011)
Bechhofer, S.: OWL: web ontology language. In: Encyclopedia of Information Science and Technology, vol. 63(45), 2nd edn., pp. 990–996 (2004)
Zeng, L., et al.: QoS-aware middleware for Web services composition. IEEE Trans. Softw. Eng. 3(4), 449–470 (2004)
Pujara, J., Miao, H., Getoor, L., Cohen, W.: Knowledge graph identification. In: Alani, H., et al. (eds.) ISWC 2013. LNCS, vol. 8218, pp. 542–557. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-41335-3_34
Xu, Z.-L., Sheng, Y.P., He, L.-R., Wang, Y.F.: Review on knowledge graph techniques. J. Univ. Electron. Sci. Technol. China 45, 589–606 (2016)
Xin, W.: Realizing Semantic Web services Description with OWL -S Ontology. New Technology of Library & Information Service (2005)
Sheng, B., Zhang, C., Yin, X., et al.: Common intelligent semantic matching engines of cloud manufacturing service based on OWL-S. Int. J. Adv. Manuf. Technol. 84(1–4), 103–118 (2016)
Kanthavel, R., Maheswari, K., Padmanabhan, N.: Information retrieval based on semantic matching approach in web service discovery. Int. J. Comput. Appl. 64(16), 54–56 (2013)
Purohit, L., Kumar, S.: Web service selection using semantic matching. In: International Conference on Advances in Information Communication Technology and Computing (2016)
Zhang, T., Liu, Y.S.: Semantic Web-based approach to simulation services dynamic discovery. Comput. Eng. Appl. 43(32), 15–19 (2007)
Song, L.L., Qun, L.I.: Research on simulation model description ontology and its matching model. Comput. Eng. Appl. 44(30), 6–12 (2008)
Li, T., Li, B.H., Chai, X.D.: Layered simulation service description framework oriented to cloud simulation. Comput. Integr. Manuf. Syst. 18(9), 2091–2098 (2012)
Cheng, C., Chen, A.Q.: Study on cloud service evaluation index system based on QoS. Appl. Mech. Mater. 742, 683–687 (2015)
Zhang, T., Liu, Y., Zha, Y.: Optimal approach to QoS-driven simulation services composition. J. Syst. Simul. 21(16), 4990–4994 (2009)
Liu, J., Sun, J., Jiang, L.: A QoS evaluation model for cloud computing. Comput. Knowl. Technol. 6(31), 8801–8803, 8806 (2010)
T. M. Organization: Resource Description Framework (RDF). Encyclopedia of GIS, pp. 6–19 (2004)
Xiong, S., Zhu, F., Yao, Y.P., Tang, W.J.: A description method of cloud simulation model resources based on knowledge graph. In 4th International Conference on Cloud Computing and Big Data Analytics, Chengdu, pp. 655–663. IEEE (2019)
Huang, Y.: The Research on Evaluation Model of Cloud Service Based on QoS and Application. Zhejiang Gongshang University (2013)
Acknowledgment
This work was supported in part by the National Natural Science Foundation of China (no. 61903368).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Xiong, S., Zhu, F., Yao, Y., Tang, W. (2019). Simulation Model Selection Method Based on Semantic Search in Cloud Environment. In: Tan, G., Lehmann, A., Teo, Y., Cai, W. (eds) Methods and Applications for Modeling and Simulation of Complex Systems. AsiaSim 2019. Communications in Computer and Information Science, vol 1094. Springer, Singapore. https://doi.org/10.1007/978-981-15-1078-6_19
Download citation
DOI: https://doi.org/10.1007/978-981-15-1078-6_19
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-1077-9
Online ISBN: 978-981-15-1078-6
eBook Packages: Computer ScienceComputer Science (R0)