OnTimeDetect: Dynamic Network Anomaly Notification in perfSONAR Deployments

To monitor and diagnose bottlenecks on network paths used for large-scale data transfers, there is an increasing trend to deploy measurement frameworks such as perfSONAR. These deployments use Web-services to expose vast data archives of current and historic measurements, which can be queried across end-to-end multi-domain network paths. Consequently, there has arisen a need to develop automated techniques and intuitive tools that help analyze these measurements for detecting and notifying prominent network anomalies such as plateaus in both real-time and offline manner. In this paper, we present a dynamically adaptive plateau-detection (APD) scheme and its implementation in our “OnTimeDetect” tool to enable consumers of perfSONAR measurements within the data-intensive scientific communities in overcoming their existing limitations of network anomaly detection and notification. We empirically evaluate our APD scheme in terms of accuracy, agility and scalability by using measurement traces collected by OnTimeDetect tool from worldwide perfSONAR deployments in HPC communities.