This Article 
 Bibliographic References 
 Add to: 
Managing Deadline Miss Ratio and Sensor Data Freshness in Real-Time Databases
October 2004 (vol. 16 no. 10)
pp. 1200-1216
The demand for real-time data services is increasing in many applications including e-commerce, agile manufacturing, and telecommunications network management. In these applications, it is desirable to execute transactions within their deadlines, i.e., before the real-world status changes, using fresh (temporally consistent) data. However, meeting these fundamental requirements is challenging due to dynamic workloads and data access patterns in these applications. Further, transaction timeliness and data freshness requirements may conflict. In this paper, we define average/transient deadline miss ratio and new data freshness metrics to let a database administrator specify the desired quality of real-time data services for a specific application. We also present a novel QoS management architecture for real-time databases to support the desired QoS even in the presence of unpredictable workloads and access patterns. To prevent overload and support the desired QoS, the presented architecture applies feedback control, admission control, and flexible freshness management schemes. A simulation study shows that our QoS-aware approach can achieve a near zero miss ratio and perfect freshness, meeting basic requirements for real-time transaction processing. In contrast, baseline approaches fail to support the desired miss ratio and/or freshness in the presence of unpredictable workloads and data access patterns.

[1] R. Abbott and H. Garcia-Molina, Scheduling Real-Time Transactions: A Performance Evaluation ACM Trans. Database System, vol. 17, pp. 513-560, 1992.
[2] ActiveMedia Research, Real Numbers behind 'Net Profits,http:/, 2000.
[3] B. Adelberg, H. Garcia-Molina, and B. Kao, Applying Update Streams in a Soft Real-Time Database System Proc. ACM SIGMOD, 1995.
[4] J.R. Haritsa, M.J. Carey, and M. Livny, On Being Optimistic about Real-Time Constraints Proc. ACM Symp. Principles of Database Systems (PODS), 1990.
[5] M. Hsu and B. Zhang, Performance Evaluation of Cautious Waiting ACM Trans. Database Systems, vol. 17, no. 3, pp. 477-512, 1992.
[6] J. Huang, J.A. Stankovic, K. Ramamritham, and D. Towsley, Experimental Evaluation of Real-Time Optimistic Concurrency Control Schemes Proc. Int'l Conf. Very Large Databases (VLDB), 1991.
[7] K.D. Kang, QoS-Aware Real-Time Data Management PhD thesis, Univ. of Virginia, May 2003.
[8] K.D. Kang, S.H. Son, J.A. Stankovic, and T.F. Abdelzaher, A QoS-Sensitive Approach for Timeliness and Freshness Guarantees in Real-Time Databases Proc. 14th Euromicro Conf. Real-Time Systems, June 2002.
[9] S. Kim, S.H. Son, and J.A. Stankovic, Performance Evaluation on a Real-Time Database Proc. IEEE Real-Time Technology and Applications Symp., 2002.
[10] J. Lee and S.H. Son, Performance of Concurrency Control Algorithms for Real-Time Database Systems Performance of Concurrency Control Mechanisms in Centralized Database Systems, Prentice Hall, 1996.
[11] B. Li and K. Nahrstedt, "A Control-Based Middleware Framework for Quality of Service Adaptations," IEEE J. Selected Areas in Comm., vol. 17, no. 9, Sept. 1999, pp. 1632-1650.
[12] K.J. Lin, S. Natarajan, and J.W.S. Liu, Imprecise Results: Utilizing Partial Computations in Real-Time Systems Proc. Real-Time System Symp., Dec. 1987.
[13] C. Lu, J.A. Stankovic, G. Tao, and S.H. Son, Feedback Control Real-Time Scheduling: Framework, Modeling, and Algorithms Real-Time Systems, special issue on control-theoretical approaches to real-time computing, vol. 23, nos. 1/2, May 2002.
[14] Moneyline Telerate Plus,http:/, 2002.
[15] G. Ozsoyoglu, S. Guruswamy, K. Du, and W. Hou, “Time-Constrained Query Processing in CASE-DB,” IEEE Trans. Knowledge and Data Eng., pp. 865-884, Dec. 1995.
[16] C.L. Phillips and H.T. Nagle, Digital Control System Analysis and Design., Third ed., Prentice Hall, 1995.
[17] K. Ramamritham, Real-Time Databases Int'l J. Distributed and Parallel Databases, vol. 1, no. 2, 1993.
[18] S.H. Son, R. Mukkamala, and R. David, “Integrating Security and Real-Time Requirements Using Covert Channel Capacity,” IEEE Trans. Knowledge and Data Eng., vol. 12, no. 6, Nov./Dec. 2000.
[19] The TimesTen Team, In-Memory Data Management for Consumer Transactions The Times Ten Approach Proc. ACM SIGMOD, 1999.
[20] S. Vrbsky, APPROXIMATE: A Query Processor that Produces Monotonically Improving Approximate Answers PhD thesis, Univ. of Illinois at Urbana-Champaign, 1993.
[21] G. Weikum, C. Hasse, A. Mönkeberg, and P. Zabback, The COMFORT Automatic Tuning Project Information Systems, vol. 19, no. 5, pp. 381-432, 1994.
[22] G. Weikum, A. Möenkeberg, C. Hasse, and P. Zabback, Self-Tuning Database Technology and Information Services: From Wishful Thinking to Viable Engineering Proc. Int'l Conf. Very Large Databses (VLDB), 2002.
[23] R. Zhang, C. Lu, T.F. Abdelzaher, and J.A. Stankovic, ControlWare: A Middleware Architecture for Feedback Control of Software Performance Proc. Int'l Conf. Distributed Computing Systems, 2002.

Index Terms:
Real-time database, deadline miss ratio, sensor data freshness, QoS management.
Kyoung-Don Kang, Sang H. Son, John A. Stankovic, "Managing Deadline Miss Ratio and Sensor Data Freshness in Real-Time Databases," IEEE Transactions on Knowledge and Data Engineering, vol. 16, no. 10, pp. 1200-1216, Oct. 2004, doi:10.1109/TKDE.2004.61
Usage of this product signifies your acceptance of the Terms of Use.