GEAP: A Generic Approach to Predicting Workload Bursts for Web Hosted Events

A number of recent research contributions in workload forecasting aim to confront the challenge facing many web applications of maintaining QoS in the face of fluctuating workload. Many of these demonstrate good prediction accuracy for periodic and long-term workload trends, but they exhibit poor accuracy when faced with predicting the magnitude, profile, and time of non-periodic bursts. It is such workload bursts that have been known to bring down numerous e-commerce and other web-based systems during events like online sales, as well as product, and result announcements. In this paper, we leverage the implicit link that often exists between such events and workload bursts, and we contribute: a generic approach that can make use of a given event’s definition to forecast the time, magnitude and profile of the event’s associated workload burst; a burst prediction accuracy metric for evaluating the efficacy of burst prediction methods; and an evaluation to showcase the generic applicability of event aware prediction across multiple domains, using real workload traces from three different domains.