Cognitive behavior analysis framework for fault prediction in cloud computing

Complex computing systems, including clusters, grids, clouds and skies, are becoming the fundamental tools of green and sustainable ecosystems of future. However, they can also pose critical bottlenecks and ignite disasters. The complexity and high number of variables could easily go beyond the capacity of any analyst or traditional operational research paradigm. In this work, we introduce a multi-paradigm, multi-layer and multi-level behavior analysis framework which can adapt to the behavior of a target complex system. It not only learns and detects normal and abnormal behaviors, it could also suggest cognitive responses in order to increase the system resilience and its grade. The multi-paradigm nature of the framework provides a robust redundancy in order to cross-cover possible hidden aspects of each paradigm. After providing the high-level design of the framework, three different paradigms are discussed. We consider the following three paradigms: Probabilistic Behavior Analysis, Simulated Probabilistic Behavior Analysis, and Behavior-Time Profile Modeling and Analysis. To be more precise and because of paper limitations, we focus on the fault prediction in the paper as a specific event-based abnormal behavior. We consider both spontaneous and gradual failure events. The promising potential of the framework has been demonstrated using simple examples and topologies. The framework can provide an intelligent approach to balance between green and high probability of completion (or high probability of availability) aspects in computing systems.