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In this paper, we present a detailed evaluation of a set of well-known Machine Learning classifiers in front of dynamic and non-deterministic software anomalies. The system state prediction is based on monitoring system metrics. This allows software proactive rejuvenation to be triggered automatically. Random Forest approach achieves validation errors less than 1% in comparison to the well-known ML algorithms under avaluation. In order to reduce automatically the number of monitored parameters, needed to predict software anomalies, we analyze Lasso Regularization technique jointly with the Machine Learning classifiers to evaluate how the prediction accuracy could be guaranteed within an acceptable threshold. This allows to reduce drastically (around 60% in the best case) the number of monitoring parameters. The framework, based on ML and Lasso regularization techniques, has been validated using an e-commerce environment with Apache Tomcat server, and MySql database server., Postprint (author’s final draft)

Unplanned system outages have a negative impact on company revenues and image. While the last decades have seen a lot of efforts from industry and academia to avoid them, they still happen and their impact is increasing. According to many studies, one of the most important causes of these outages is software aging. Software aging phenomena refers to the accumulation of errors, usually provoking resource contention, during long running application executions, like web applications, which normally cause applications/systems hang or crash. Determining the software aging root cause failure, not the resource or resources involved in, is a huge task due to the growing day by day complexity of the systems. In this paper we present a monitoring framework based on Aspect Programming to monitor the resources used by every application component in runtime. Knowing the resources used by every component of the application we can determine which components are related to the software aging. Furthermore, we present a case study where we evaluate our approach to determine in a web application scenario, which components are involved in the software aging with promising results., Postprint (published version)

The growing complexity of software systems is resulting in an increasing number of software faults. According to the literature, software faults are becoming one of the main sources of unplanned system outages, and have an important impact on company benefits and image. For this reason, a lot of techniques (such as clustering, fail-over techniques, or server redundancy) have been proposed to avoid software failures, and yet they still happen. Many software failures are those due to the software aging phenomena. In this work, we present a detailed evaluation of our chosen machine learning prediction algorithm (M5P) in front of dynamic and non-deterministic software aging. We have tested our prediction model on a three-tier web 12EE application achieving acceptable prediction accuracy against complex scenarios with small training data sets. Furthermore, we have found an interesting approach to help to determine the root cause failure: The model generated by machine learning algorithms., Postprint (published version)

As long as virtualization has been introduced in data centers, it has been opening new chances for resource management. Now, it is not just used as a tool for consolidating underused nodes and save power, it also allows new solutions to well-known challenges, such as fault tolerance or heterogeneity management. Virtualization helps to encapsulate Web-based applications or HPC jobs in virtual machines and see them as a single entity which can be managed in an easier way. This paper proposes a new scheduling policy to model and manage a virtualized data center which mainly focuses on the allocation of VMs in data center nodes according to multiple facets while optimizing the provider’s profit. In particular, it considers energy efficiency, virtualization overheads, fault tolerance, and SLA violation penalties, while adding the ability to outsource resources to external providers. Using our approach, a data center can improve the provider’s benefit by 15% and get a power reduction while solving well-known challenges, such as fault tolerance and outsourcing, in a better a more intuitive way that typical approaches do., Peer Reviewed, Postprint (published version)