Non-intrusive Estimation of QoS Degradation Impact on E-Commerce User Satisfaction

With the massification of high speed Internet access, recent industry consumer reports show that Web site performance is increasingly becoming a key feature in determining user satisfaction, and finally, a decisive factor in whether a user will purchase on a Web site or even return to it. Traditional Web infrastructure capacity planning has focused on maintaining high throughput and availability on Web sites, optimizing the number of servers to serve peak hours to minimize costs. However, as we will show with our study, the conversion rate-the fraction of users that purchase on a site-is higher at peak hours, where systems are more exposed to suffer overload. In this article we propose a methodology to determine the thresholds of user satisfaction as the QoS delivered by an online business degrades, and to estimate its effects on actual sales. The novelty of the presented technique is that it does not involve any intrusive manipulation of production systems, but a learning process over historic sales data that is combined with system performance measurements. The methodology has been applied to Atra palo.com, a top national Travel and Booking site. For our experiments, we were given access to a 3 year long sales history dataset, as well as actual HTTP and resource consumption logs for several weeks. Obtained results enable autonomic resource managers to set best performance goals and optimize the number of server according to the workload, without surpassing the thresholds of user satisfaction and maximizing revenue for the site.