Monitoring et détection d'anomalie par apprentissage dans des infrastructures virtualisées

Carla Sauvanaud 1
1 LAAS-TSF - Équipe Tolérance aux fautes et Sûreté de Fonctionnement informatique
LAAS - Laboratoire d'analyse et d'architecture des systèmes [Toulouse]
Abstract : Nowadays, the development of virtualization technologies as well as the development of the Internet contributed to the rise of the cloud computing model. A cloud computing enables the delivery of configurable computing resources while enabling convenient, on-demand network access to these resources. Resources hosted by a provider can be applications, development platforms or infrastructures. Over the past few years, computing systems are characterized by high development speed, parallelism, and the diversity of task to be handled by applications and services. In order to satisfy their Service Level Agreements (SLA) drawn up with users, cloud providers have to handle stringent dependability demands. Ensuring these demands while delivering various services makes clouds dependability a challenging task, especially because providers need to make their services available on demand. This task is all the more challenging that users expect cloud services to be at least as dependable as traditional computing systems. In this manuscript, we address the problem of anomaly detection in cloud services. A detection strategy for clouds should rely on several principal criteria. In particular it should adapt to workload changes and reconfigurations, and at the same time require short configurations durations and adapt to several types of services. Also, it should be performed online and automatic. Finally, such a strategy needs to tackle the detection of different types of anomalies namely errors, preliminary symptoms of SLA violation and SLA violations. We propose a new detection strategy based on system monitoring data. The data is collected online either from the service, or the underlying hypervisor(s) hosting the service. The strategy makes use of machine learning algorithms to classify anomalous behaviors of the service. Three techniques are used, using respectively algorithms with supervised learning, unsupervised learning or using a technique exploiting both types of learning. A new anomaly detection technique is developed based on online clustering, and allowing to handle possible changes in a service behavior. A cloud platform was deployed so as to evaluate the detection performances of our strategy. Moreover a fault injection tool was developed for the sake of two goals : the collection of service observations with anomalies so as to train detection models, and the evaluation of the strategy in presence of anomalies. The evaluation was applied to two case studies : a database management system and a virtual network function. Sensitivity analyzes show that detection performances of our strategy are high for the three anomaly types. The context for the generalization of the results is also discussed.
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Informatique [cs]. INSA Toulouse, 2016. Français
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Carla Sauvanaud. Monitoring et détection d'anomalie par apprentissage dans des infrastructures virtualisées. Informatique [cs]. INSA Toulouse, 2016. Français. 〈tel-01445648〉

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