This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
Shared Data Allocation in a Mobile Computing System: Exploring Local and Global Optimization
April 2005 (vol. 16 no. 4)
pp. 374-384
ASCII Text
x 
 
Wen-Chih Peng, Ming-Syan Chen, "Shared Data Allocation in a Mobile Computing System: Exploring Local and Global Optimization," IEEE Transactions on Parallel and Distributed Systems, vol. 16, no. 4, pp. 374-384, April, 2005.
 
 
BibTex
x
 
@article{ 10.1109/TPDS.2005.50,
author = {Wen-Chih Peng and Ming-Syan Chen},
title = {Shared Data Allocation in a Mobile Computing System: Exploring Local and Global Optimization},
journal ={IEEE Transactions on Parallel and Distributed Systems},
volume = {16},
number = {4},
issn = {1045-9219},
year = {2005},
pages = {374-384},
doi = {http://doi.ieeecomputersociety.org/10.1109/TPDS.2005.50},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Parallel and Distributed Systems
TI - Shared Data Allocation in a Mobile Computing System: Exploring Local and Global Optimization
IS - 4
SN - 1045-9219
SP374
EP384
EPD - 374-384
A1 - Wen-Chih Peng,
A1 - Ming-Syan Chen,
PY - 2005
KW - User moving patterns
KW - mobile computing
KW - shared data allocation
KW - mobile databases.
VL - 16
JA - IEEE Transactions on Parallel and Distributed Systems
ER -
 

Abstract—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. 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 closeness measure 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.

[1] D. Barbara, “Mobile Computing and Databases— A Survey,” IEEE Trans. Knowledge and Data Eng., vol. 11, no. 1, pp. 108-117, Jan./Feb. 1999.
[2] B. Bruegge and B. Bennington, “Applications of Mobile Computing and Communication,” IEEE Personal Comm., pp. 64-71, Feb. 1996.
[3] K. Buchanan, R. Fudge, D. McFarlane, T. Phillips, A.A. Sasaki, and H. Xia, “IMT-2000: Service Provider's Perspective,” IEEE Personal Comm., vol. 4, no. 4, pp. 8-13, Aug. 1997.
[4] M.-S. Chen, J.-S. Park, and P.S. Yu, “Efficient Data Mining for Path Traversal Patterns,” IEEE Trans. Knowledge and Data Eng., vol. 10, no. 2, pp. 209-221, Apr. 1998.
[5] G. Cho and L.F. Marshall, “An Efficient Location and Routing Scheme for Mobile Computing Environments,” IEEE J. Selected Areas in Comm., vol. 13, no. 5, pp. 868-879, June 1995.
[6] T.H. Cormen, C.E. Leiserson, and R.L. Rivest, Introduction to Algorithm. MIT Press, 1990.
[7] E. Dahlman, B. Gudmundson, M. Nilsson, and A. Skold, “UMTS/IMT-2000 Based on Wideband CDMA,” IEEE Comm. Magazine, vol. 36, no. 9, pp. 70-80, Sept. 1998.
[8] N. Davies, G.S. Blair, K. Cheverst, and A. Friday, “Supporting Collaborative Application in a Heterogeneous Mobile Environment,” Computer Comm., specical issue on mobile computing, 1996.
[9] M.H. Dunham, A. Helal, and S. Balakrishnan, “A Mobile Transaction Model That Captures Both the Data and Movement Behavior,” ACM J. Mobile Networks and Applications, vol. 2, pp. 149-162, 1997.
[10] EIA/TIA, Cellular Radio Telecomm. Intersystem Operations, 1991.
[11] T. Imielinski and B.R. Badrinath, “Mobile Wireless Computing,” Comm. ACM, vol. 37, no. 10, pp. 18-28, Oct. 1994.
[12] Intelligent Transportation Systems, http:/www.artimis.org/, 2004.
[13] J. Jannink, D. Lam, N. Shivakumar, J. Widom, and D. Cox, “Efficient and Flexible Location Management Techniques for Wireless Communication Systems,” ACM J. Wireless Networks, vol. 3, no. 5, pp. 361-374, 1997.
[14] J. Jing, A. Helal, and A. Elmagarmid, “Client-Server Computing in Mobile Environments,” ACM Computing Surveys, vol. 31, no. 2, pp. 117-157, June 1999.
[15] D.N. Knisely, S. Kumar, S. Laha, and S. Nanda, “Evolution of Wireless Data Services: IS-95 to cdma2000,” IEEE Comm. Magazine, pp. 140-149, Oct. 1998.
[16] N. Krishnakumar and R. Jain, “Escrow Techniques for Mobile Sales and Inventory Applications,” ACM J. Wireless Network, vol. 3, no. 3, pp. 235-246, July 1997.
[17] Y.-B. Lin, “GSM Network Signaling,” ACM Mobile Computing and Comm., vol. 1, no. 2, pp. 11-16, 1997.
[18] L.B. Milstein, “Wideband Code Division Multiple Access,” IEEE J. Seclected Areas in Comm., vol. 18, no. 8, pp. 1344-1355, Aug. 2000.
[19] M. Nicola and M. Jarke, “Performance Modeling of Distributed and Replicated Databases,” IEEE Trans. Knowledge and Data Eng., vol. 12, no. 4, pp. 645-672, July 2000.
[20] W.-C. Peng and M.-S. Chen, “Mining User Moving Patterns for Personal Data Allocation in a Mobile Computing System,” Proc. 29th Int'l Conf. Parallel Processing (ICPP 2000), Aug. 2000.
[21] E. Pitoura and G. Samaras, “Locating Objects in Mobile Computing,” IEEE Trans. Knowledge and Data Eng., vol. 13, no. 4, pp. 571-592, July/Aug. 2001.
[22] A. Samukic, “UMTS Universal Mobile Telecommunications System: Development of Standards for the Third Generation,” IEEE Trans. Vehicular Technology, vol. 47, no. 4, pp. 1099-1104, Nov. 1998.
[23] M. Satyanarayanan, “Mobile Information Access,” IEEE Personal Comm., pp. 26-33, Feb. 1996.
[24] S. Shekhar, A. Fetterer, and D. Lui, “Genesis: An Approach to Data Disemination in Advanced Travel Information Systems,” IEEE Data Eng. Bull., vol. 19, no. 3, pp. 37-45, 1996.
[25] N. Shivakumar, J. Jannink, and J. Widom, “Per-User Profile Replication in Mobile Environments: Algorithms, Analysis and Simulation Results,” ACM J. Mobile Networks and Applications, vol. 2, pp. 129-140, 1997.
[26] U. Varshney and R. Vetter, “Emerging Mobile and Wireless Networks,” Comm. ACM, vol. 43, no. 6, pp. 73-81, June 2000.
[27] O. Wolfson, S. Jajodia, and Y. Huang, “An Adaptive Data Replication Algorithm,” ACM Trans. Database Systems, vol. 22, no. 4, pp. 255-314, June 1997.
[28] A. Wolski, “Database Replication for the Mobile Era,” Tutorial of the Proc. 18th Int'l Conf. Data Eng., Feb. 2002.
[29] H.-K. Wu, M.-H. Jin, J.-T. Horng, and C.-Y. Ke, “Personal Paging Area Design Based on Mobile's Moving Behaviors,” Proc. IEEE Infocom 2001, pp. 21-30, Apr. 2001.

Index Terms:
User moving patterns, mobile computing, shared data allocation, mobile databases.
Citation:
Wen-Chih Peng, Ming-Syan Chen, "Shared Data Allocation in a Mobile Computing System: Exploring Local and Global Optimization," IEEE Transactions on Parallel and Distributed Systems, vol. 16, no. 4, pp. 374-384, April 2005, doi:10.1109/TPDS.2005.50
Usage of this product signifies your acceptance of the Terms of Use.