Short-term prediction models for server management in Internet-based contexts

Modern Internet applications run on top of complex system infrastructures where several runtime management algorithms have to guarantee high performance, scalability and availability. This paper aims to offer a support to runtime algorithms that must take decisions on the basis of historical and predicted load conditions of the internal system resources. We propose a new class of moving filtering techniques and of adaptive prediction models that are specifically designed to deal with runtime and short-term forecast of time series which originate from monitors of system resources of Internet-based servers. A large set of experiments confirm that the proposed models improve the prediction accuracy with respect to existing algorithms and they show stable results for different workload scenarios.