A methodology for workload characterization of E-commerce sites
Proceedings of the 1st ACM conference on Electronic commerce
Link prediction and path analysis using Markov chains
Proceedings of the 9th international World Wide Web conference on Computer networks : the international journal of computer and telecommunications netowrking
In search of invariants for e-business workloads
Proceedings of the 2nd ACM conference on Electronic commerce
Rule-assisted prefetching in Web-server caching
Proceedings of the ninth international conference on Information and knowledge management
Characterizing reference locality in the WWW
DIS '96 Proceedings of the fourth international conference on on Parallel and distributed information systems
Mining web logs for prediction models in WWW caching and prefetching
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Web caching and replication
Knowledge and Information Systems
Session-Based Admission Control: A Mechanism for Peak Load Management of Commercial Web Sites
IEEE Transactions on Computers
A Real-Time Evolutionary Algorithm for Web Prediction
WI '03 Proceedings of the 2003 IEEE/WIC International Conference on Web Intelligence
Session Based Differentiated Quality of Service Admission Control for Web Servers
ICCNMC '03 Proceedings of the 2003 International Conference on Computer Networks and Mobile Computing
Selective Markov models for predicting Web page accesses
ACM Transactions on Internet Technology (TOIT)
Designing an overload control strategy for secure e-commerce applications
Computer Networks: The International Journal of Computer and Telecommunications Networking
Learning PDFA with asynchronous transitions
ICGI'10 Proceedings of the 10th international colloquium conference on Grammatical inference: theoretical results and applications
Adaptive admission control algorithm in a QoS-aware Web system
Information Sciences: an International Journal
A meta-controller method for improving run-time self-architecting in SOA systems
Proceedings of the 5th ACM/SPEC international conference on Performance engineering
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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.