st-andrews.ac.uk

st-andrews.ac.uk Autonomously detecting and recovering from faults is one approach for reducing the operational complexity and costs associated with managing computing environments. We pre-sent a novel methodology for autonomously generating in-vestigation leads that help identify systems faults. Specifi-cally, when historical feature data is present, Hidden Markov Models can be used to heuristically identify the root cause of a fault in an unsupervised manner. This approach improves the state of the art by allowing self-healing systems to detect faults with greater autonomy than existing methodologies, and thus further reduce operational costs