Filtering Alerts on Cloud Monitoring Systems
Fotios Voutsas, John Violos, Aris Leivadeas · 2023
Recent advances in cloud computing and data centers have increased the demands for monitoring the network infrastructure and the applications that it hosts. The monitoring processes let network administrators to be aware of the status of the physical and logical units that compose their system. Since the goal of next generation networks is to minimise the administrators’ intervention, the alerting systems should minimize the frequency of notifications, emphasizing on critical scenarios such as when a monitoring metric surpasses a threshold or an anomalous behaviour is detected. However, current monitoring tools flood network administrators with hundreds of notifications every day. In this paper, we propose a binary classification approach, in order to decide if the administrators should be notified through monitoring alerts or not. To do so, our framework is build upon real monitoring logs and alerts, that show how the administrators reacted when receiving an alert. Extensive simulation results assess the performance of various classification approaches and reveal that random forests are great candidates for the binary classification alerting system that we propose, in terms of classification efficiency and computational overhead.