A General Nash Equilibrium Semantic Cache Algorithm in a Sensor Grid Database
Sensor grid databases are powerful, distributed, self- organizing systems that allow in-network query processing and offer a user friendly SQL-like application development. We propose an adaptation of a well-known cache-based optimization and cache replacement policy to this context. Since the data are distributed and the sensor nodes are mobile, the cost model is more complicated than in traditional query optimization, because it should account for several factors, including the semantics, location and time. Therefore, we need a trade-off between those constraints. Our approach is based on a theoretical foundation for the game and balance problem. In summary, we propose an approach that (i) Based on the Nash Equilibrium scheme, an application of three scalar coefficients is proposed in term of the analysis of the relationship among semantic, time and location factors. (ii) After that, we specialize and summarize the general Nash Equilibrium scheme utilizing the equal correlation coefficient among the new and old vectors to find the point of Nash equilibrium and resolves the scalar coefficients. (iii) It uses these scalar coefficient to attain an optimum cost model of the semantic cache, for query optimization in the context of distributed query processing. (iv) We emphasize that this method can extend to any finite-dimension, which are other schemes cannot do. Extended simulation results indicate that our scheme outperforms existing approaches in terms of both the response time and the cache hit ratio.
KeywordsSensor Grid Database Semantic Cache Location Dependent Query Query Optimization Nash Equilibrium
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- 1.Ewen, S., Schelter, S., Tzoumas, K., Warneke, D., Markl, V.: Iterative parallel data processing with stratosphere: an inside look. In: Proceedings of the ACM SIGMOD International Conference on Management of Data, pp. 1053–1056 (2013)Google Scholar
- 3.Li, Y., Jin, D., Hui, P., Su, L., Zeng, L.: Revealing contact interval patterns in large scale urban vehicular ad hoc networks. In: Proceedings of the ACM SIGCOMM 2012 Conference on Applications, Technologies, Architectures, and Protocols for Computer Communications (2012)Google Scholar
- 4.Ren, Q., Dunham, M.H.: Using clustering for effective management of a semantic cache in mobile computing. In: Mobile 1999 Proceedings of the 1st ACM International Workshop on Data Engineering for Wireless and Mobile Access (1999)Google Scholar
- 7.Xu, B., Vafaee, F., Wolfson, O.: In-network query processing in mobile p2p databases. In: Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (2009)Google Scholar