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Journal of Systems and Software
Volume 80, Issue 7, July 2007, Pages 1102-1119
Dynamic Resource Management in Distributed Real-Time Systems
 
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doi:10.1016/j.jss.2006.10.027    How to Cite or Link Using DOI (Opens New Window)
Published by Elsevier Inc.

A context-aware cache structure for mobile computing environments

Stylianos Drakatosa, Corresponding Author Contact Information, E-mail The Corresponding Author, Niki Pissinoua, E-mail The Corresponding Author, Kia Makkia, E-mail The Corresponding Author and Christos Douligerisb, E-mail The Corresponding Author

aTelecommunication and Information Technology Institute, Department of Electrical and Computer Engineering, Florida International University Miami, FL 33174, USA bDepartment of Informatics, University of Piraeus, Piraeus 18534, Greece

Received 14 April 2006; 
revised 5 October 2006; 
accepted 10 October 2006. 
Available online 28 November 2006.

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Abstract

This paper proposes a cache management method that maintains a mobile terminal’s cache content by prefetching data items with maximum benefit and evicting cache data entries with minimum benefit. The data item benefit is evaluated based on the user’s query context which is defined as a set of constraints (predicates) that define both the movement pattern and the information context requested by the mobile user. A context-aware cache is formed and maintained using a set of neighboring locations (called the prime list) that are restricted by the validity of the data fetched from the server. Simulation results show that the proposed strategy, using different levels of granularity, can greatly improve system performance in terms of the cache hit ratio.

Keywords: Context-aware information service; Mobile computing; Location-awareness; Caching; Prefetching

Article Outline

1. Introduction
2. Previous work
3. Mobile architecture
3.1. Modeling user mobility
3.1.1. Location granularity
3.2. The client’s semantic cache description
4. The cache prefetching components
4.1. The valid scope concept
4.2. Movement pattern – future cells for prefetching
4.3. Query pattern and cache management
5. Prefetching cost analysis model
5.1. Numerical results
6. Experimental study
6.1. The environment
6.2. The client cache model
7. Performance evaluation
7.1. Impact of cache size
7.2. Impact of affinity factor
7.3. Impact of data item types
7.4. Impact of query delay
7.5. Impact of movement speed
8. Conclusions and future work
Acknowledgements
Appendix A. Cell numbers and the distance formula
Appendix B. Transition probabilities evaluation
References
Vitae





















Journal of Systems and Software
Volume 80, Issue 7, July 2007, Pages 1102-1119
Dynamic Resource Management in Distributed Real-Time Systems
 
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