SimSim: A Service Discovery Method Preserving Content Similarity and Spatial Similarity in P2P Mobile Cloud
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Mobile cloud has become a new computing paradigm such that services are accessible in any place and at any time. Despite its promising prospect, challenges arise due to unreliable channel condition and limited bandwidth in wireless communication, dynamic route establishment due to node mobility, difficulties in associating request to relevant service providers, and complication in service deployment. To ensure the fairness of resource allocation and network load balance, it is necessary to consider strategies for distributing services. In this paper, we propose SimSim, a service discovery scheme based on keywords search which preserves content similarity and spatial similarity. A mapping from a keyword set of services to a bit vector with identical hash is designed to preserve content similarity. The proposed technique applies a hierarchical hash clustering model and investigates the strategies of service deployment and discovery. By mapping the services characterized by keywords to the Gray space, SimSim offers similar services at close geographical proximity. Extensive simulations have been conducted to assess the proposed system.
KeywordsService discovery Gray space Content similarity Spatial similarity Hierarchical hash clustering
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The authors would like to thank the anonymous reviewers for their valuable comments and suggestions. This work is supported by Major Scientific and Technological Project of Fujian, China under Grant No. 2013HZ0001-4, and Experimental Teaching Reform project of Fujian University of Technology under Grant No. SJ2015019.
- 3.Cai, Z., Chen, C.: Demand-driven task scheduling using 2d chromosome genetic algorithm in mobile cloud. In: International Conference on Progress in Informatics and Computing (PIC). IEEE, pp 539–545 (2014)Google Scholar
- 6.Carlini, E., Coppola, M., Ricci, L.: Probabilistic dropping in push and pull dissemination over distributed hash tables. In: IEEE International Conference on Computer and Information Technology, pp 47–52 (2011)Google Scholar
- 11.Datar, M., Immorlica, N., Indyk, P., Mirrokni, V.S.: Locality-sensitive hashing scheme based on p-stable distributions. In: Proceedings of the 20th Annual Symposium on Computational Geometry. ACM, pp 253–262 (2004)Google Scholar
- 22.Liao, J., Yang, D., Li, T., Qi, Q., Wang, J., Sun, H.: Fusion feature for LSH-based image retrieval in a cloud datacenter. Multimed. Tools Appl. 75(15), 405–15,427 (2016)Google Scholar
- 25.Pan, J.S., Kong, L., Sung, T.W., Pei-Wei, T., Snasel, W.: α-fraction first strategy for hirarchical wireless sensor networks. J. Internet Technol. 19(6), 1717–1726 (2018)Google Scholar
- 28.Rowstron, A., Druschel, P.: Pastry: Scalable, distributed object location and routing for large-scale peer-to-peer systems. In: IFIP/ACM International Conference on Distributed Systems Platforms (Middleware), pp 1–22 (2001)Google Scholar
- 29.Salgado, C., Cheema, M.A., Ali, M.E.: Continuous monitoring of range spatial keyword query over moving objects. World Wide Web (2017)Google Scholar
- 32.Selimi, M, Cerdà-Alabern, L, Freitag, F, Veiga, L, Sathiaseelan, A, Crowcroft, J: A lightweight service placement approach for community network micro-clouds. Journal of Grid Computing (2018)Google Scholar
- 35.Stoica, I, Morris, R, Karger, D, et al.: Chord: A scalable peer-to-peer lookup service for internet applications. In: Proceedings of ACM SIGCOMM, pp 1–12 (2001)Google Scholar