This Article 
 Bibliographic References 
 Add to: 
Quality of Service Guarantee for Temporal Consistency of Real-Time Transactions
August 2006 (vol. 18 no. 8)
pp. 1097-1110
The More-Less (ML) scheme has been shown to be an efficient approach for maintaining temporal consistency of real-time data objects. Although ML provides a deterministic guarantee in temporal consistency, the number of update transactions that can be supported in a system is limited. This is due to its use of the worst-case computation time in deriving deadlines and periods of update transactions. This paper studies the problem of temporal consistency maintenance where a certain degree of temporal inconsistency is tolerable. A suite of Statistical More-Less (SML) approaches are proposed to explore the trade-off between quality of service (QoS) of temporal consistency and the number of supported transactions. It begins with a base-line algorithm, SML-BA, which provides the requested QoS of temporal consistency. Then, SML with Optimization (SML-OPT) is proposed to further improve the QoS by better utilizing the excess processor capacity. Finally, SML-OPT is enhanced with a Slack Reclaiming scheme (SML-SR). The reclaimed slacks are used to process jobs whose required computation time is larger than the guaranteed computation time. Simulation experiments are conducted to compare the performance of these schemes (SML-BA, SML-OPT, and SML-SR) together with the deterministic More-Less and Half-Half schemes. The results show that the SML schemes are effective in trading the schedulability of transactions for the QoS guaranteed. Moreover, SML-SR performs best and offers a significant QoS improvement over SML-BA and SML-OPT.

[1] A. Atlas and A. Bestavros, “Slack Stealing Job Admission Control Scheduling,” Technical Report 98-009, Boston Univ., May 1998.
[2] A. Atlas and A. Bestavros, “Statistical Rate Monotonic Scheduling,” Proc. IEEE Real-Time Systems Symp., Dec. 1998.
[3] R. Gerber, S. Hong, and M. Saksena, “Guaranteeing End-to-End Timing Constraints by Calibrating Intermediate Processes,” Proc. IEEE 15th Real-Time Systems Symp., Dec. 1994.
[4] K.D. Kang, S. Son, J. Stankovic, and T. Abdelzaher, “A QoS-Sensitive Approach for Timeliness and Freshness Guarantees in Real-Time Databases,” Proc. EuroMicro Real-Time Systems Conf., June 2002.
[5] B. Kao, K.Y. Lam, B. Adelberg, R. Cheng, and T. Lee, “Maintaining Temporal Consistency of Discrete Objects in Soft Real-Time Database Systems,” IEEE Trans. Computers, vol. 52, no. 3, pp. 373-389, Mar. 2003.
[6] M. Harchol-Balter, “The Effect of Heavy-Tailed Job Size. Distributions on Computer System Design,” Proc. ASA-IMS Conf. Applications of Heavy Tailed Distributions in Economics, Eng., and Statistics, June 1999.
[7] S. Ho, T. Kuo, and A.K. Mok, “Similarity-Based Load Adjustment for Static Real-Time Transaction Systems,” Proc. 18th IEEE Real-Time Systems Symp., 1997.
[8] T. Kuo and A.K. Mok, “Real-Time Data Semantics and Similarity-Based Concurrency Control,” Proc. IEEE 13th Real-Time Systems Symp., Dec. 1992.
[9] T. Kuo and A.K. Mok, “SSP: A Semantics-Based Protocol for Real-Time Data Access,” Proc. IEEE 14th Real-Time Systems Symp., Dec. 1993.
[10] K.Y. Lam, M. Xiong, B. Liang, and Y. Guo, “Statistical Quality of Service Guarantee for Temporal Consistency of Real-Time Data Objects,” Proc. 25th IEEE Real-Time Systems Symp., Dec. 2004.
[11] J. Leung and J. Whitehead, “On the Complexity of Fixed-Priority Scheduling of Periodic Real-Time Tasks,” Performance Evaluation, vol. 2, pp. 237-250, 1982.
[12] D. Locke, “Real-Time Databases: Real-World Requirements,” Real-Time Database Systems: Issues and Applications, pp. 83-91, 1997.
[13] E. Polak, Optimization: Algorithms and Consistent Approximations. Springer-Verlag, pp. 56-69, 1997.
[14] K. Ramamritham, “Real-Time Databases,” Distributed and Parallel Databases, vol. 1, pp. 199-226, 1993.
[15] X. Song and J.W. S. Liu, “Maintaining Temporal Consistency: Pessimistic versus Optimistic Concurrency Control,” IEEE Trans. Knowledge and Data Eng., vol. 7, no. 5, pp. 786-796, Oct. 1995.
[16] T. Tia, Z. Deng, M. Shankar, M. Storch, J. Sun, L.-C. Wu, and J.W.-S. Liu, “Probabilistic Performance Guarantees for Real-Time Tasks with Varying Computation Times,” Proc. Real-Time Technology and Applications Symp., pp. 164-173, May 1995.
[17] M. Xiong, K. Ramamritham, J.A. Stankovic, D. Towsley, R. Sivasankaran, “Scheduling Transactions with Temporal Constraints: Exploiting Data Semantics,” IEEE Trans. Knowledge and Data Eng., vol. 14, no. 5, pp. 1155-1166, 2002.
[18] M. Xiong and K. Ramamritham, “Deriving Deadlines and Periods for Real-Time Update Transactions,” IEEE Trans. Computers, vol. 53, no. 5, pp. 567-583, 2004.
[19] H. Zhou and F. Jahanian, “Real-Time Primary-Backup (RTPB) Replication with Temporal Consistency Guarantees,” Proc. Int'l Conf. Distributed Computing Systems, May 1998.

Index Terms:
Real-time database, probabilistic temporal consistency, transactions scheduling, quality of service.
Ming Xiong, Biyu Liang, Kam-Yiu Lam, Yang Guo, "Quality of Service Guarantee for Temporal Consistency of Real-Time Transactions," IEEE Transactions on Knowledge and Data Engineering, vol. 18, no. 8, pp. 1097-1110, Aug. 2006, doi:10.1109/TKDE.2006.128
Usage of this product signifies your acceptance of the Terms of Use.