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Given a number of known reference workloads, and an unknown workload, this paper deals with the problem of finding the reference workload which is most similar to the unknown one. T he depicted scenario turns to be useful in a plethora of modern information system applications. We name this problem as coarse-grained workload classification, because, instead of characterizing the unknown workload in terms of finer behaviors, such as CPU, memory, disk or network intensive patterns, we classify the whole unknown workload as one of the (possible) reference workloads. Reference workloads represent a category of workloads that are relevant in a given applicative environment. In particular, we focus our attention on the classification problem described above in the special case represented by virtualized environments. Today, Virtual Machines (VMs) have become very popular because they offer important advantages to modern computing environments such as cloud computing or server farms. In virtualization frameworks, workload classification is very useful for accounting, security reasons or user profiling. Hence, our research makes more sense in such environments, and it turns to be very useful in a special context like cloud computing, which is emerging at now. In this respect, our approach consists in running several machine-learning-based classifiers of different workload models, and then deriving the best classifier produced by the Dempster-Shafer fusion, in order to magnify the accuracy of the final classification. Experimental assessment and analysis c1ealry confirm the benefits deriving from our classification framework.

Data mining allows for hidden patterns to be brought to light in large data sets. This paper aims to apply data mining on real-life data showing Greenhouse Gas Emissions for countries within the European Union. Greenhouse Gasses are gasses which are released into the atmosphere, trapping the infrared radiation from the sun and causing an effect called the Greenhouse Effect. This effect contributes to global Climate Change and is a topical issue. Using the K-means clustering algorithm, a model is produced in order to provide a deeper insight into the emissions of the industrial sectors of the UK, France and Italy. The model is intended to be of use to those in governmental authority when decisions on emissions within individual industries are to be made.