Towards Mitigation of the Light Network Load Performance Penalty of the Network Link Outlier Factor (NLOF)

Detecting and localizing faults in communication networks is critical to maintaining reliable and efficient network operations. The Network Link Outlier Factor with Most Likely Link (NLOF: MLL) algorithm has demonstrated its potential to automate this task but suffers from significant performance degradation under low network load conditions, where limited network flow data reduces its ability to localize faults. This thesis proposes and evaluates the performance of a synthetic traffic generation algorithm to be used with NLOF:MLL. This algorithm strategically injects synthetic flows that supplement the insufficient real network flows, thereby improving NLOF:MLL's performance under low-load conditions. Specifically, we select network flow host pairs such that core network link coverage is heuristically maximized. We use a set of Mininet experiments to evaluate the performance of our synthetic traffic generation algorithm. While our findings are not conclusive, they suggest that synthetic traffic generation may provide a promising avenue for improving NLOF's performance. Further work is needed to isolate the effects of different traffic parameters and to determine how synthetic flows can best support accurate fault localization across varying network conditions.