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

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