Internet Web servers: workload characterization and performance implications
IEEE/ACM Transactions on Networking (TON)
Self-similarity in World Wide Web traffic: evidence and possible causes
IEEE/ACM Transactions on Networking (TON)
Generating representative Web workloads for network and server performance evaluation
SIGMETRICS '98/PERFORMANCE '98 Proceedings of the 1998 ACM SIGMETRICS joint international conference on Measurement and modeling of computer systems
ACM Transactions on Computer Systems (TOCS)
Characteristics of WWW Client-based Traces
Characteristics of WWW Client-based Traces
Proceedings of the 4th ACM SIGCOMM conference on Internet measurement
BLINC: multilevel traffic classification in the dark
Proceedings of the 2005 conference on Applications, technologies, architectures, and protocols for computer communications
Automation and customization of rendered web pages
Proceedings of the 18th annual ACM symposium on User interface software and technology
Ajax in Action
Generating a privacy footprint on the internet
Proceedings of the 6th ACM SIGCOMM conference on Internet measurement
Unexpected means of protocol inference
Proceedings of the 6th ACM SIGCOMM conference on Internet measurement
CARENA: a tool to capture and replay Web navigation sessions
E2EMON '05 Proceedings of the End-to-End Monitoring Techniques and Services on 2005. Workshop
Youtube traffic characterization: a view from the edge
Proceedings of the 7th ACM SIGCOMM conference on Internet measurement
A comparative analysis of web and peer-to-peer traffic
Proceedings of the 17th international conference on World Wide Web
The new web: characterizing AJAX traffic
PAM'08 Proceedings of the 9th international conference on Passive and active network measurement
Understanding website complexity: measurements, metrics, and implications
Proceedings of the 2011 ACM SIGCOMM conference on Internet measurement conference
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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.