Mining association rules for admission control and service differentiation in e-commerce applications

Workload demands in e-commerce applications are very dynamic in nature, therefore it is essential for internet service providers to manage server resources effectively to maximise total revenue in server overloading situations. In this paper, a data mining technique is applied to a typical e-commerce application model for identification of composite association rules that capture user navigation patterns. Two algorithms are then developed based on the derived rules for admission control, service differentiation and priority scheduling. Our approach takes the following into consideration: a) only final purchase requests result in company revenue; b) any other request can potentially lead to a final purchase, depending upon the likelihood of the navigation sequence that starts from current request and leads to final purchase; c) service differentiation and priority assignment are based on aggregated confidence and average support of the composite association rules. As identification of composite association rules and computation of confidence and support of the rules can be pre-computed offline, the proposed approach incurs minimum performance overheads. The evaluation results suggest that the proposed approach is effective in terms of request management for revenue maximisation