Client-Side Monitoring of HTTP Clusters Using Machine Learning Techniques

Abstract

Large online web sites are supported in the back-end by a cluster of servers behind a load balancer. Ensuring proper operation of the cluster with minimal monitoring efforts from the load balancer is necessary to ensure performance. Previous monitoring efforts require extensive data from the system and fail to include the client perspective. We monitor the cluster using machine learning techniques that process data collected and uploaded by web clients, an approach that might complement system-side information. To experiment our solution, we trained the machine learning algorithms in a cluster of 10 machines with a load balancer and evaluated the results of these algorithms when one of the machines is overloaded. While a fine-grained view of the state of the machines, may require much effort to accomplish, given the compensation effect of the remaining healthy machines, the results show that we can achieve a coarse grained view of the entire system, to produce relevant insight about the cluster.