This paper proposes a framework for soft real-time text classification system, which use control theory as a scientific underpinning, rather than ad hoc solutions. In order to provide real-time guarantee, two control loops are adopted. The feed forward control loop estimates the suitable number of classifiers according to the current workload, while the feedback control loop provides fine-grained control to the number of classifiers that perform imprecise computation. The soft real-time classification system can accommodate to the change of workload and transitional overload. The theory analysis and experiments result further prove its effectiveness: the variation range of the average response time is kept within ±3% of the desired value; the computational resource is dynamically reallocated and reclaimed. © Springer-Verlag Berlin Heidelberg 2005.
CITATION STYLE
Wang, H., Chen, Y., & Dai, Y. (2005). A soft real-time web news classification system with double control loops. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3739 LNCS, pp. 81–90). Springer Verlag. https://doi.org/10.1007/11563952_8
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