Client-side black-box monitoring for web sites

In spite of their growing maturity, current web monitoring tools are unable to observe all operating conditions. For example, clients in different geographical locations might get very diverse latencies to the server; the network between client and server might be slow; or third-party servers with external page resources might underperform. Ultimately, only the clients can determine whether a site is up and running in good conditions. In this paper, we use the response times experienced by clients, to infer about server and network performance. The goal is to detect internal and external bottlenecks doing black-box monitoring, in particular CPU (internal) and network (external). We aim to determine to what extent are the clients able to tell one type of bottleneck from the other, i.e., what kind of information do the server and network leak, regarding their operating conditions. To answer this question, we resort to an empirical approach. We submit an HTTP server and network to a large number of operating conditions and train two machine learning algorithms, a linear and a non-linear one, to identify the cause of the congestion affecting the system. Results show that the server and network leak information to a level of detail that allows sorting out CPU from network bottlenecks, or even a combination of the two, in a large spectrum of cases. This suggests that a black-box monitoring approach is not only possible, but promising, as it may complement traditional white-box approaches.