A Transformation Technique for Scheduling Broadcast Programs of Multiple-Item Queries

A Transformation Technique for Scheduling Broadcast Programs of Multiple-Item Queries

Jen-Ya Wang
Copyright: © 2012 |Volume: 4 |Issue: 4 |Pages: 16
ISSN: 1938-0259|EISSN: 1938-0267|EISBN13: 9781466612341|DOI: 10.4018/jghpc.2012100104
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MLA

Wang, Jen-Ya. "A Transformation Technique for Scheduling Broadcast Programs of Multiple-Item Queries." IJGHPC vol.4, no.4 2012: pp.52-67. http://doi.org/10.4018/jghpc.2012100104

APA

Wang, J. (2012). A Transformation Technique for Scheduling Broadcast Programs of Multiple-Item Queries. International Journal of Grid and High Performance Computing (IJGHPC), 4(4), 52-67. http://doi.org/10.4018/jghpc.2012100104

Chicago

Wang, Jen-Ya. "A Transformation Technique for Scheduling Broadcast Programs of Multiple-Item Queries," International Journal of Grid and High Performance Computing (IJGHPC) 4, no.4: 52-67. http://doi.org/10.4018/jghpc.2012100104

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Abstract

In wireless environments, many mobile users may raise queries for accessing multiple data items, e.g., stock information or traffic condition, simultaneously. Most queries are identical. That is, many users may have an interest in some popular data items. Because data broadcast can offer unlimited users shareable information at the same time, a broadcast server is usually employed to disseminate all the data items periodically. Intuitively, popular data items should be scheduled and transmitted to users more efficiently than ordinary ones, so these users can thus save access time. To improve customer satisfaction, the author considers a broadcast program scheduling problem in such an environment and aims to minimize mobile users’ worst access time as well as their battery power consumption by generating near-optimal broadcast programs. The author provides theoretical analysis as a foundation of mapping the problem to another domain (i.e., from unit item to unit fragment) and this transformation makes the problem easy to solve. Moreover, an O(N logN) algorithm is proposed for this NP-hard problem. Finally, experimental results show that access time can be reduced by carefully scheduling broadcast programs. It suggests that other similar optimization problems can be solved similarly.

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