Abstract
More and more applications involve processing continuous data streams, and the data stream management system (DSMS) is designed to deal with such data streams. Due to features of large volume and stochastic arrival, DSMS must process data stream efficiently in order to avoid system memory exhaustion and reduce the data access latency, to satisfy requirements of the application requirement. One of the key factors, which significantly impact the system performance significantly, is the scheduling strategy adopted by the DSMS. Chain scheduling is an operator-based scheduling strategy for DSMS, which has near-optimal in terms of run-time memory usage. FIFO strategy achieves optimal performance in terms of data access latency. Inspired by the two important scheduling strategies, Chain and FIFO, we propose two novel adaptive strategies for DSMS, ASCF and CSS, which efficiently deal with the varying input load in terms of both memory usage and data access latency. To give a fair comparison performance with other competing strategies, we design thorough simulation experiment and run different strategies under the same system environment. The outcomes of simulation experiment demonstrate the potential benefits and advantages of ASCF and CSC.
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References
Golab, L., Tamer Ozsu, M.: Issues in Data Stream Management. SIGMOD RECORD 32(2), 5–14 (2003)
Schmidt, S., Legler, T., Schaller, D., Lehner, W.: Real-time Scheduling for Data Stream Management System. In: Proceding of the 17th Euromicro Conference on Real-Time Systems, pp. 167–176 (2005)
Hellerstein, J., Franklin, M., et al.: Adaptive query processing: Technology in evolution. IEEE Data Engineering Bulletin 23(2), 7–18 (2000)
Motwani, R., Widom, J., et al.: Query processing, approximation, and resource management in a data stream management system. In: Proc. First Biennial Conf. on Innovative Data Systems Research (Jan. 2003)
Carney, D., Cetintemel, U., Cherniack, M., Convey, C., Lee, S., Seidman, G., Stonebraker, M., TatbuL, N., Zdonik, S.: Monitoring streams - a new class of data management applications. In: Proc. Of the 2002 Intl. Conf. On Very Large Data Bases (2002)
Chen, J., Dewitt, D., Tian, F., Wang, Y.: Niagaracq: A scalable continuous query system for internet databases. In: Proc. of the 2000 ACM SIGMOD Intl. Conf. on Management of Data, pp. 379–390 (2000)
Terry, D., Goldberg, D., Nichols, D., Oki, B.: Continuous queries over append-only databases. In: Proc. of the 1992 ACM SIGMOD Intl. Conf. on Management of Data, June 1992, pp. 321–330 (1992)
Sullivan, M.: Tribeca: A stream database manager for network traffic analysis. In: Proc. of the 1996 Intl. Conf. on Very Large Data Bases, Sept. 1996, p. 594 (1996)
Jiang, Q., Chakravarthy, S.: Scheduling Strategies for a Data Stream Management System. In: BNCOD 2004, pp. 16–30 (2004)
Sharaf, M.A., Chrysanthis, P.K., Labrinidis, A.: Preemptive Rate-Based Operator Scheduling in a Data Stream Management System. In: The 3rd ACS/IEEE International Conference on Computer Systems and Applications (2005)
Babcock, B., Babu, S., Datar, M., Motwani, R., Thomas, D.: Operator Scheduling in Data Stream System. VLDB J. 13(4), 333–353 (2004)
Babcock, B., Babu, S., Datar, M., Motwani, R., Widom, J.: Models and Issues in Data Stream Systems. In: PODS 2002, pp. 1–16 (2002)
Carney, D., Cetintemel, U., Cherniack, M., Convey, C., Lee, S., Seidman, G., Stonebraker, M., Tatbul, N., Zdonik, S.: Monitoring streams-a new class of data management application. In: Proc. 28th intl. Conf. on Very Large Data Bases (Aug. 2002)
Chandrasekaran, S., Franklin, M.: Streaming queries over streaming data. In: Proc. 28th Intl. Conf. on Very Large Data Bases (August 2002)
Madden, S., Shan, M., Hellerstein, J.N., Raman, V.: Continuously adaptive continuous queries over streams. In: Proc. of the 2002 ACM SIGMOD Intl. Conf. on Management of Data (June 2002)
Avnur, R., Hellerstein, J.M.: Eddies: Continuously adaptive query processing. In: Proc. Of the 2000 ACM SIGMOD Intl. Conf. on Management of Data, May 2000, pp. 261–272 (2000)
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Sun, G., Zhou, Y., Huang, Y., Zhou, Y. (2007). Adaptive Scheduling Strategy for Data Stream Management System. In: Dong, G., Lin, X., Wang, W., Yang, Y., Yu, J.X. (eds) Advances in Data and Web Management. APWeb WAIM 2007 2007. Lecture Notes in Computer Science, vol 4505. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72524-4_53
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DOI: https://doi.org/10.1007/978-3-540-72524-4_53
Publisher Name: Springer, Berlin, Heidelberg
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