A Software Architecture for the Analysis of Large Sets of Data Streams in Cloud Infrastructures

System management algorithms in private and public cloud infrastructures have to work with literally thousands of data streams generated from resource, application and event monitors. This cloud context opens two novel issues that we address in this paper: how to design a software architecture that is able to gather and analyze all information within real-time constraints, how it is possible to reduce the analysis of the huge collected data set to the investigation of a reduced set of relevant information. The application of the proposed architecture is based on the most advanced software components, and is oriented to the classification of the statistical behavior of servers and to the analysis of significant state changes. These results guide model-driven management systems to investigate only relevant servers and to apply suitable decision models considering the deterministic or non-deterministic nature of server behaviors.