A software architecture for the analysis of large sets of data streams in cloud infrastructures

System management algorithms in private andpublic cloud infrastructures have to work with literally thousands of data streams generated from resource, applicationand event monitors. This cloud context opens two novel issuesthat we address in this paper: how to design a softwarearchitecture that is able to gather and analyze all informationwithin real-time constraints; how it is possible to reduce theanalysis of the huge collected data set to the investigationof a reduced set of relevant information. The application ofthe proposed architecture is based on the most advancedsoftware components, and is oriented to the classification of thestatistical behavior of servers and to the analysis of significantstate changes. These results guide model-driven managementsystems to investigate only relevant servers and to applysuitable decision models considering the determ