We propose Cloud-based machine learning tools for enhanced Big Data applications, where the main idea is that of predicting the “next” workload occurring against the target Cloud infrastructure via an innovative ensemble-based approach that combines the effectiveness of different well-known classifiers in order to enhance the whole accuracy of the final classification, which is very relevant at now in the specific context of Big Data. The so- called workload categorization problem plays a critical role in improving the efficiency and reliability of Cloud-based big data applications. Implementation-wise, our method proposes deploying Cloud entities that participate in the distributed classification approach on top of virtual machines, which represent classical “commodity” settings for Cloud-based big data applications. Given a number of known reference workloads, and an unknown workload, in this paper we deal with the problem of finding the reference workload which is most similar to the unknown one. The depicted scenario turns out 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 out to be very useful in a special context like Cloud Computing, which is emerging now. In this respect, our approach consists of 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 clearly confirm the benefits derived from our classification framework. The running programs which produce unknown workloads to be classified are treated in a similar way. A fundamental aspect of this paper concerns the successful use of data fusion in workload classification. Different types of metrics are in fact fused together using the Dempster-Shafer theory of evidence combination, giving a classification accuracy of slightly less than 80%. The acquisition of data from the running process, the pre-processing algorithms, and the workload classification are described in detail. Various classical algorithms have been used for classification to classify the workloads, and the results are compared. پرونده مقاله

Embedded computing systems are very vulnerable to anomalies that can occur during execution of deployed software. Anomalies can be due, for example, to faults, bugs or deadlocks during executions. These anomalies can have very dangerous consequences on the systems controlled by embedded computing devices. Embedded systems are designed to perform autonomously, i.e., without any human intervention, and thus the possibility of debugging an application to manage the anomaly is very difficult, if not impossible. Anomaly detection algorithms are the primary means of being aware of anomalous conditions. In this paper, we describe a novel approach for detecting an anomaly during the execution of one or more applications. The algorithm exploits the differences in the behavior of memory reference sequences generated during executions. Memory reference sequences are monitored in real-time using the PIN tracing tool. The memory reference sequence is divided into randomly-selected blocks and spectrally described with the Discrete Cosine Transform (DCT). Experimental analysis performed on various benchmarks shows very low error rates for the anomalies tested. پرونده مقاله