Alarm prioritization and diagnosis for cellular networks

Alarm events occurring in telecommunication networks can be an invaluable tool for network operators. However, given the size and complexity of today’s networks, handling of alarm events represents a challenge in itself, due to two key aspects: high volume and lack of descriptiveness. The latter derives from the fact that not all alarm events report the actual source of failure. A failure in a higher-level managed object could result in alarm events observed on its controlled objects. In addition, alarm events may not be indicative of network distress, as many devices have automatic fallback solutions that may permit normal network operation to continue. Indeed, given the amount of equipment in a network, there can be a “normal” amount of failure that occurs on a regular basis; if each alarm is treated with equal attention, the volume can quickly become untenable. To address these shortcomings, we propose a novel framework that prioritizes and diagnoses alarm events. We rely on a priori information about the managed network structure, relationships, and fault management practices, and use a probabilistic logic engine that allows evidence and rules to be encoded as sentences in first order logic. Our work, tested using real cellular network data, achieves a significant reduction in the amount of analyzed objects in the network by combining alarms into sub-graphs and prioritizing them, and offers the most probable diagnosis outcome.