Selective resource characterization for evaluation of system dynamics

Management decisions to achieve peak performance operations, scalability and availability in distributed systems require a continuous statistical characterization of data setscoming from server and network monitors. Due to the increasing sizes of data centers and their continuous dynamicchanges, the traditional approaches that work on all datasets in a centralized way are impractical. We propose astrategy for data processing that is able to limit the analysis of the large sets of collected measures to a smaller subsetof significant information for a twofold purpose: to classifythe collected data sets in few classes characterized by similarstatistical behaviors, to evaluate the dynamics of the overallsystem and its most relevant changes. The proposed strategy works at the level of server resources and of significantaggregation of servers of the overall distributed system. Several experimental results demonstrate the feasibility of theproposed strategy that is validated in real contexts.