Cloud-based Web applications powered by new technologies such as Asynchronous Javascript and XML (Ajax) place a significant burden on network operators and enterprises to effectively manage traffic. Despite increase of their popularity, we have little understanding of characteristics of these cloud applications. Part of the problem is that there exists no systematic way to generate their workloads, observe their network behavior today and keep track of the changing trends of these applications. This paper focuses on addressing these issues by developing a tool, called AJAXTRACKER, that automatically mimics a human interaction with a cloud application and collects associated network traces. These traces can further be post-processed to understand various characteristics of these applications and those characteristics can be fed into a classifier to identify new traffic for a particular application in a passive trace. The tool also can be used by service providers to automatically generate relevant workloads to monitor and test specific applications.
['Myungjin Lee', 'Ramana Rao Kompella', 'Sumeet Singh']
AjaxTracker: active measurement system for high-fidelity characterization of AJAX applications