Further Insights Into The Network Link Outlier Factor's (NLOF) Light-Load Penalty

This research investigates the performance of the Network Link Outlier Factor with Most Likely Links (NLOF:MLL), under varying network load conditions. Earlier studies reported that the NLOF:MLL algorithm experienced a noticeable drop in fault-localization accuracy when operating in lightly loaded networks. To further examine this limitation, 240 experiments were carried out to observe how the algorithm responds as overall network load increases. The evaluation focused on the classification performance metrics: precision, recall, and F1-score. The results show that NLOF:MLL’s effectiveness improves as network load increases but that the rate of improvement slows progressively, eventually stabilizing in a pattern consistent with a logarithmic trend. The reduced performance observed under low network load occurs because low throughput range limits the variability within the NLOF:MLL clustering window, resulting in weak flow differentiation and unstable flow outlier detection. Overall, this research provides statistical evidence that NLOF:MLL follows a smooth, saturating performance curve rather than an abrupt transition, thereby clarifying the nature of its “light-load penalty” and offering insight into where refinement is needed to mitigate this penalty.