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<p><b>Abstract</b>—In this paper, we devise data allocation algorithms that can utilize the knowledge of user moving patterns for proper allocation of shared data in a mobile computing system. By employing the data allocation algorithms devised, the occurrences of costly remote accesses can be minimized and the performance of a mobile computing system is thus improved. The data allocation algorithms for shared data, which are able to achieve <it>local optimization</it> and <it>global optimization</it>, are developed. Local optimization refers to the optimization that the likelihood of local data access by an <it>individual</it> mobile user is maximized whereas global optimization refers to the optimization that the likelihood of local data access by <it>all</it> mobile users is maximized. Specifically, by exploring the features of local optimization and global optimization, we devise algorithm SD-local and algorithm SD-global to achieve local optimization and global optimization, respectively. In general, the mobile users are divided into two types, namely, frequently moving users and infrequently moving users. A measurement, called <it>closeness measure</it> which corresponds to the amount of the intersection between the set of frequently moving user patterns and that of infrequently moving user patterns, is derived to assess the quality of solutions provided by SD-local and SD-global. Performance of these data allocation algorithms is comparatively analyzed. From the analysis of SD-local and SD-global, it is shown that SD-local favors infrequently moving users whereas SD-global is good for frequently moving users. The simulation results show that the knowledge obtained from the user moving patterns is very important in devising effective data allocation algorithms which can lead to prominent performance improvement in a mobile computing system.</p>
User moving patterns, mobile computing, shared data allocation, mobile databases.

W. Peng and M. Chen, "Shared Data Allocation in a Mobile Computing System: Exploring Local and Global Optimization," in IEEE Transactions on Parallel & Distributed Systems, vol. 16, no. , pp. 374-384, 2005.
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