PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Self-Adaptive Utility-Based Web Session Management.
Nicolas Poggi, Toni Moreno, Josep Lluis Berral, Ricard Gavaldà and Jordi Torres
Computer Networks Volume 53, Number 10, pp. 1712-1721, 2009. ISSN 1389-1286


In the Internet, where millions of users are a click away from your site, being able to dynamically classify the workload in real time, and predict its short term behavior, is crucial for proper self-management and business efficiency. As workloads vary significantly according to current time of day, season, promotions and linking, it becomes impractical for some ecommerce sites to keep over-dimensioned infrastructures to accommodate the whole load. When server resources are exceeded, session-based admission control systems allow maintaining a high throughput in terms of properly finished sessions and QoS for a limited number of sessions; however, by denying access to excess users, the website looses potential customers. In the present study we describe the architecture of AUGURES, a system that learns to predict Web user's intentions for visiting the site as well its resource usage. Predictions are made from information known at the time of their first request and later from navigational clicks. For this purpose we use machine learning techniques and Markov-chain models. The system uses these predictions to automatically shape QoS for the most profitable sessions, predict short-term resource needs, and dynamically provision servers according to the expected revenue and the cost to serve it. We test the AUGURES prototype on access logs from a high-traffic, online travel agency, obtaining promising results.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:User Modelling for Computer Human Interaction
Learning/Statistics & Optimisation
ID Code:5812
Deposited By:Ricard Gavaldà
Deposited On:08 March 2010