Algorithms for Managing QoS for Real-Time Data Services Using Imprecise Computation
Lately the demand for real-time data services has increased in applications where it is desirable to process user requests within their deadlines using fresh data. The real-time data services are usually provided by a real-time database (RTDB). Here, since the workload of the RTDBs cannot be precisely predicted, RTDBs can become overloaded. As a result, deadline misses and freshness violations may occur. To address this problem we propose a QoS-sensitive approach to guarantee a set of requirements on the behavior of RTDBs. Our approach is based on imprecise computation, applied on both data and transactions. We propose two algorithms to dynamically balance the workload and the quality of the data and transactions. Performance evaluations show that our algorithms give a robust and controlled behavior of RTDBs, in terms of transaction and data quality, even for transient overloads and with inaccurate run-time estimates of the transactions.
KeywordsData Object Freshness Manager Admission Controller User Transaction Miss Percentage
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- 2.Amirijoo, M.: Algorithms for managing QoS for real-time data services using imprecise computation. Master’s Thesis Report LiTH-IDA-Ex-02/90 (2002), www.ida.liu.se/~rtslab/master/past
- 3.Buttazzo, G.C., Abeni, L.: Adaptive workload managment through elastic scheduling. Journal of Real-time Systems 23(1/2) (July/September 2002); Special Issue on Control-Theoretical Approaches to Real-Time ComputingGoogle Scholar
- 4.Chen, X., Cheng, A.M.K.: An imprecise algorithm for real-time compressed image and video transmission. In: Proceedings of the Sixth International Conference on Computer Communications and Networks, pp. 390–397 (1997)Google Scholar
- 7.Kang, K., Son, S.H., Stankovic, J.A.: Service differentiation in real-time main memory databases. In: Proceedings of 5th IEEE International Symposium on Object-oriented Real-time Distributed Computing (April 2002)Google Scholar
- 8.Labrinidis, A., Roussopoulos, N.: Update propagation strategies for improving the quality of data on the web. The VLDB Journal, 391–400 (2001)Google Scholar
- 9.Liu, J.W.S., Lin, K., Shin, W., Yu, A.C.-S.: Algorithms for scheduling imprecise computations. IEEE Computer 24(5) (May 1991)Google Scholar
- 10.Lu, C., Stankovic, J.A., Tao, G., Son, S.H.: Feedback control real-time scheduling: Framework, modeling and algorithms. Journal of Real-time Systems 23(1/2) (July/September 2002); Special Issue on Control-Theoretical Approaches to Real- Time ComputingGoogle Scholar
- 11.Malinski, P., Sandri, S., Reitas, C.: An imprecision-based image classifier. In: The 10th IEEE International Conference on Fuzzy Systems, pp. 825–828 (2001)Google Scholar
- 12.Millan-Lopez, V., Feng, W., Liu, J.W.S.: Using the imprecise-computation technique for congestion control on a real-time traffic switching element. In: International Conference on Parallel and Distributed Systems, pp. 202–208 (1994)Google Scholar
- 13.Parekh, S., Gandhi, N., Hellerstein, J., Tilbury, D., Jayram, T., Bigus, J.: Using control theory to achieve service level objectives in performance managment. Journal of Real-time Systems 23(1/2) (July/September 2002); Special Issue on Control-Theoretical Approaches to Real-Time ComputingGoogle Scholar
- 14.Ramamritham, K.: Real-time databases. International Journal of Distributed and Parallel Databases (1) (1993)Google Scholar