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 measu...

• Type: Conference
• Year: 2010
• Where: MASCOTS
• Authors: Prasad Calyam, Jialu Pu, Weiping Mandrawa, Ashok Krishnamurthy
• Keywords: MASCOTS 2010, Measurement, Modeling And Simulation, Multi-domain Network Paths, Network Paths