This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
MULS: A General Framework of Providing Multilevel Service Quality in Sequential Data Broadcasting
October 2007 (vol. 19 no. 10)
pp. 1433-1447
In recent years, data broadcasting becomes a promising technique to design a mobile information system with power conservation, high scalability and high bandwidth utilization. In many applications, the query issued by a mobile client corresponds to multiple items which should be accessed in a sequential order. In this paper, we study the scheduling approach in such a sequential data broadcasting environment. Explicitly, we propose a general framework referred to as MULS (standing for MUlti-Level Service) for an information system. There are two primary stages in MULS: on-line scheduling and optimization procedure. In the first stage, we propose an On- Line Scheduling algorithm (denoted by OLS) to allocate the data items into multiple channels. As for the second stage, we devise an optimization procedure SCI, standing for Sampling with Controlled Iteration, to enhance the quality of broadcast programs generated by algorithm OLS. Procedure SCI is able to strike a compromise between effectiveness and efficiency by tuning the control parameters. According to the experimental results, we show that algorithm OLS with procedure SCI outperforms the approaches in prior works prominently in both effectiveness (i.e., the average access time of mobile users) and efficiency (i.e., the complexity of the scheduling algorithm). Therefore, by cooperating algorithm OLS with procedure SCI, the proposed MULS framework is able to generate broadcast programs with flexibility of providing different service qualities under different requirements of effectiveness and efficiency: in the dynamic environment in which the access patterns and information contents change rapidly, the parameters used in SCI will perform online scheduling with satisfactory service quality. As for the static environment in which the query profile and the database are updated infrequently, larger values of parameters are helpful to generate an optimized broadcast program, indicating the advantageous feature of MULS.

[1] Sony Corp., http:/www.sony.co.jp/, 2006.
[2] S. Acharya, R. Alonso, M.J. Franklin, and S.B. Zdonik, “Broadcast Disks: Data Management for Asymmetric Communications Environments,” Proc. ACM SIGMOD Int'l Conf. Management of Data (SIGMOD '95), pp. 199-210, May 1995.
[3] S. Acharya, M.J. Franklin, and S.B. Zdonik, “Disseminating Updates on Broadcast Disks,” Proc. 22nd Int'l Conf. Very Large Data Bases (VLDB '96), pp. 354-365, Sept. 1996.
[4] S. Acharya and S. Muthukrishnan, “Scheduling On-Demand Broadcasts: New Metrics and Algorithms,” Proc. MobiCom '98, pp. 43-54, 1998.
[5] D. Aksoy and M. Franklin, “RxW: A Scheduling Approach for Large-Scale On-Demand Data Broadcast,” IEEE/ACM Trans. Networking, vol. 7, no. 6, pp. 846-860, 1999.
[6] D. Aksoy, M.J. Franklin, and S. Zdonik, “Data Staging for On-Demand Broadcast,” Proc. 27th Int'l Conf. Very Large Data Bases (VLDB '01), pp. 571-580, Sept. 2001.
[7] D. Aksoy and M.S. Leung, “Pull vs. Push: A Quantitative Comparison for Data Broadcast,” Proc. IEEE Global Telecomm. Conf. (GLOBECOM '04), 2004.
[8] M.-S. Chen, P.S. Yu, and K.-L. Wu, “Optimizing Index Allocation for Sequential Data Broadcasting in Wireless Mobile Computing,” IEEE Trans. Knowledge and Data Eng., vol. 15, no. 1, Jan./Feb. 2003.
[9] Y. Chung and M. Kim, “Efficient Data Placement for Wireless Broadcast,” Distributed and Parallel Database, vol. 9, no. 2, Mar. 2001.
[10] Y.D. Chung and M.H. Kim, “QEM: A Scheduling Method for Wireless Broadcast Data,” Proc. Sixth Int'l Conf. Database Systems for Advanced Applications (DASFAA '99), 1999.
[11] C.-H. Hsu, G. Lee, and A.L.P. Chen, “A Near Optimal Algorithm for Generating Broadcast Programs on Multiple Channels,” Proc. 10th ACM Int'l Conf. Information and Knowledge Management (CIKM '01), pp. 303-309, Nov. 2001.
[12] C.L. Hu and M.S. Chen, “On-Line Scheduling Sequential Objects for Dynamic Information Dissemination,” Proc. IEEE Global Telecomm. Conf. (GLOBECOM '05), 2005.
[13] J.-L. Huang and M.-S. Chen, “Broadcasting Dependent Data for Ordered Queries without Replication in a Multi-Channel Mobile Environment,” Proc. 19th IEEE Int'l Conf. Data Eng. (ICDE '03), Mar. 2003.
[14] H.P. Hung and M.S. Chen, “On Exploring Channel Allocation in the Diverse Data Broadcasting Environment,” Proc. 25th IEEE Int'l Conf. Distributed Computing Systems (ICDCS '05), 2005.
[15] H.P. Hung, J.W. Huang, J.L. Huang, and M.S. Chen, “Scheduling Dependent Items in Data Broadcasting Environments,” Proc. 21st Ann. ACM Symp. Applied Computing (SAC '06), 2006.
[16] T. Imielinski, S. Viswanathan, and B.R. Badrinath, “Data on Air: Organization and Access,” IEEE Trans. Knowledge and Data Eng., vol. 9, no. 3, pp. 353-372, May/June 1997.
[17] J.-L. Huang and M.-S. Chen, “Dependent Data Broadcasting for Unordered Queries in a Multiple Channel Mobile Environment,” IEEE Trans. Knowledge and Data Eng., vol. 16, no. 6, June 2004.
[18] L. Kaufman and P.J. Rousseeuw, Finding Groups in Data: An Introduction to Cluster Analysis. John Wiley & Sons, 1990.
[19] G. Lee and S.C. Lo, “Broadcast Data Allocation for Efficient Access of Multiple Data Items in Mobile Environments,” Mobile Networks and Applications, vol. 8, no. 4, 2003.
[20] C.M. Liu and K.F. Lin, “Efficient Scheduling Algorithms for Disseminating Dependent Data in Wireless Mobile Environments,” Proc. IEEE Int'l Conf. Wireless Networks, Comm., and Mobile Computing (WIRELESSCOM '05), 2005.
[21] S.-C. Lo and A.L. Chen, “Optimal Index and Data Allocation in Multiple Broadcast Channels,” Proc. 16th IEEE Int'l Conf. Data Eng. (ICDE '00), pp. 293-302, 2000.
[22] F. Martinez, J. Gonzalez, and I. Stojmenovic, “A Parallel Hill Climbing Algorithm for Pushing Dependent Data in Clients-Providers-Servers Systems,” Proc. Seventh Int'l Symp. Computer and Comm. (ISCC '02), 2002.
[23] A. Nanopoulos, D. Katsaros, and Y. Manolopoulos, “Effective Prediction of Web-User Accesses: A Data Mining Approach,” Proc. 12th ACM SIGKDD Workshop Web Mining and Web Usage Analysis (WebKDD '01), 2001.
[24] V. Padmanabhan and L. Qiu, “The Content and Access Dynamics of a Busy Web Site: Findings and Implications,” Proc. ACM Conf. Applications, Technologies, Architectures, and Protocols for Computer Comm. (SIGCOMM '00), 2000.
[25] W.-C. Peng and M.-S. Chen, “Efficient Channel Allocation Tree Generation for Data Broadcasting in a Mobile Computing Environment,” Wireless Networks, vol. 9, no. 2, pp. 117-129, 2003.
[26] N. Prabhu and V. Kumar, “Data Scheduling for Multi-Item and Transactional Requests in On-Demand Broadcast,” Proc. Sixth Int'l Conf. Mobile Data Management (MDM '05), 2005.
[27] A. Si and H.V. Leong, “Query Optimization for Broadcast Database,” Data and Knowledge Eng., vol. 23, no. 9, 1999.
[28] W.-G. Yee, S.B. Navathe, E. Omiecinski, and C. Jermaine, “Efficient Data Allocation over Multiple Channels at Broadcast Servers,” IEEE Trans. Computers, vol. 51, no. 10, pp. 1231-1236, Oct. 2002.
[29] B. Zheng, X. Wu, X. Jin, and D.L. Lee, “TOSA: A Near-Optimal Scheduling Algorithm for Multi-Channel Data Broadcast,” Proc. Sixth Int'l Conf. Mobile Data Management (MDM '05), 2005.
[30] G.K. Zipf, Human Behaviour and the Principle of Least Effort. Addison-Wesley, 1949.

Index Terms:
Data Broadcasting, mobile computing, sequential data broadcasting, ordered-dependency, multi-level service quality
Citation:
Hao-Ping Hung, Ming-Syan Chen, "MULS: A General Framework of Providing Multilevel Service Quality in Sequential Data Broadcasting," IEEE Transactions on Knowledge and Data Engineering, vol. 19, no. 10, pp. 1433-1447, Oct. 2007, doi:10.1109/TKDE.2007.1072
Usage of this product signifies your acceptance of the Terms of Use.