Social Plane for Recommenders in Network Performance Expectation Management

Multi-domain end-to-end network performance monitoring federations such as perfSONAR are increasingly being used in Big Data application management. They rely on trustworthy collaborative measurement intelligence to identify and diagnose network anomaly events that impact application performance. Large volumes of end-to-end measurement traces are generated on a daily basis, and new Big Data analysis techniques are needed to isolate network-wide anomaly event(s) and to diagnose the root-cause(s). In addition, not all network operators and application users have enough knowledge and experience to understand the anomaly events. The lack of a platform for sharing knowledge and working collaboratively makes it difficult to isolate and diagnose network-wide anomaly events quickly and accurately. In this paper, we define a “social plane” that relies on recommended measurements based on “content-based filtering” and “collaborative filtering” approaches to enable network performance expectation management. Based on similarity analysis, the content-based filtering facilitates users to subscribe to useful measurements, and the collaborative filtering promotes users to share knowledge on anomaly symptoms. Using real perfSONAR measurements and synthetic events, we show the effectiveness of our social plane approach within a SoyKB Big Data application case study using social network creation and mingling of experts. Our experimental results show that our measurements recommendation scheme has high precision, recall, and accuracy, as well as efficiency in terms of the time taken for large volume measurement trace analysis.