Load prediction models in web-based systems

Run-time management of modern Web-based services requires the integration of several algorithms and mechanisms for job dispatching, load sharing, admission control, overload detection. All these algorithms should take decisions on the basis of present and/or future load conditions of the system resources. In particular, we address the issue of predicting future resource loads under real-time constraints in the context of Internet-based systems. In this situation, it is extremely difficult to deduce a representative view of a system resource from collected raw measures that show very large variability even at different time scales. For this reason, we propose a two-step approach that first aims to get a representative view of the load trend from measured raw data, and then applies a load prediction algorithm to load trends. This approach is suitable to support different decision systems even for highly variable contexts and is characterized by a computational complexity that is compatible to run-time decisions. The proposed models are applied to a multi-tier Web-based system, but the results can be extended to other Internet-based contexts where the systems are characterized by similar workloads and resource behaviors.