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Shared Data Allocation in Mobile

Computing-Using Local optimizationand Global optimization

Phanikanth.ch10102111

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Mobile Computing

cellular

bluetooth

WiFi

UWB

satellite

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Shared Data

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An example scenario of shared dataallocation problem

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In this Paper, we device data allocation algorithms that can utilize the

knowledge of user moving patterns for proper allocation of shareddata.

Performance of Mobile computing system can be improved by properallocation of shared data.

Based on moving behaviors users can be further divided into twotypes:

Frequently moving usersInfrequently moving users

Closeness measure which corresponds to the amount of the intersectionbetween the set of frequently moving user patterns and that of infrequently moving user patterns

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Local optimization

Local optimization refers to the optimization that the likelihood of localdata access by an individual mobile user is maximized , meaning that theprobability of average local access is maximized.

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Global optimization

Global optimization refers to the optimization that the likelihood of local dataaccess by all mobile users is maximized , meaning that the number of totallocal access is maximized.

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Algorithm SD-local

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Algorithm SD-global

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Local hit ratios of mobile users using SD-local and SD-global are higherthan that using DF.

Infrequently moving users U2,U3,U4 will have better local access hitswhen using SD-local than using SD-global.Frequently moving user U1 performs better under SD-globalSD-Local favors infrequently moving users.

SD-Global favors frequently moving users.

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T he Impact of Closeness Measure

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Comp aris on analysis of SD-Local and SD-gl ob al

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Conclusion

we devised data allocation schemes that utilize the knowledge of usermoving patterns for proper allocation of shard data in a mobilecomputing system.

Algorithms SD-local and SD-global are devised to achieve localoptimization and global optimization.

SD-local favors infrequently moving users and SD-global favors frequentlymoving users.

T he knowledge obtained from the user moving patterns is very importantin devising effective shared data allocation algorithms.