Allocation of Shared Data Based on Mobile User Movement

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 local optimization and global optimization, are developed. Local optimization refers to the optimization that the likelihood of local data access by an individual mobile user is maximized whereas global optimization refers to the optimization that the likelihood of local data access by all mobile users is maximized. By exploring the corresponding features, we devise algorithm SD-local and algorithm SD-global to achieve local optimization and global optimization, respectively. 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.