A Soft Real-Time Web News Classification System with Double Control Loops

摘要

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.