Multi-domain network performance monitoring (NPM) federations, such as perfSONAR rely on collaborative measurement intelligence to identify network anomaly events and diagnose performance bottlenecks affecting data-intensive science applications. In this paper, we present a novel measurement recommendation scheme to assist network operators and application users by recommending pertinent samples from a pool of measurement data involving multiple domains to detect and troubleshoot correlated network anomaly events. The recommendations are based on the principles of content-based filtering. Such recommendations are complimented with Bayesian Inference based domain reputation meta-information to strengthen the veracity information of the recommended traces. Using actual long-term and short-term perfSONAR traces, we analyze recommendation results and show: a) how the content-based filter recommends the most pertinent traces based on their attributes, and b) the time-variant characteristics of domain reputation. Finally, using synthetic traces, we show the effectiveness of our proposed measurements recommendation scheme in accurately identifying anomaly events for an exemplar use case, and also show how our content filter based recommendation scheme performs better in terms of false alarms in comparison to: a) recommendations that consider partial trace features for filtering, and b) greedy recommendation approaches based on random trace selection.
['Yuanxun Zhang', 'Saptarshi Debroy', 'Prasad Calyam']
Network measurement recommendations for performance bottleneck correlation analysis