Characterizing Secure Dynamic Web Applications Scalability
IPDPS '05 Proceedings of the 19th IEEE International Parallel and Distributed Processing Symposium (IPDPS'05) - Papers - Volume 01
Session-Based Adaptive Overload Control for Secure Dynamic Web Applications
ICPP '05 Proceedings of the 2005 International Conference on Parallel Processing
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
DEEP-SaM - Energy-Efficient Provisioning Policies for Computing Environments
GECON '09 Proceedings of the 6th International Workshop on Grid Economics and Business Models
Learning PDFA with asynchronous transitions
ICGI'10 Proceedings of the 10th international colloquium conference on Grammatical inference: theoretical results and applications
On some method for limited services selection
International Journal of Intelligent Information and Database Systems
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In the Web environment, user identification is becoming a major challenge for admission control systems on high traffic sites. When a web server is overloaded there is a significant loss of throughput when we compare finished sessions and the number of responses per second; longer sessions are usually the ones ending in sales but also the most sensitive to load failures. Session-based admission control systems maintain a high QoS for a limited number of sessions, but does not maximize revenue as it treats all non-logged sessions the same. We present a novel method for learning to assign priorities to sessions according to the revenue that will generate. For this, we use traditional machine learning techniques and Markov-chain models. We are able to train a system to estimate the probability of the user's purchasing intentions according to its early navigation clicks and other static information. The predictions can be used by admission control systems to prioritize sessions or deny them if no resources are available, thus improving sales throughput per unit of time for a given infrastructure. We test our approach on access logs obtained from a high-traffic online travel agency, with promising results.