Panoptes: A monitoring architecture and framework for supporting autonomic Clouds

The essential characteristics of Cloud computing are scalability, elasticity, and heterogeneous resource pooling. However, managing these systems is challenging due to their complexity and dynamism. Using Autonomic Computing to achieve self-management is a prominent approach to respond these challenges. The fundamental basis for the decision making process of such systems is the updated status of the system and its operational context. In this paper, we propose a monitoring architecture devised for private Cloud that focuses on providing data analytics capabilities to the monitoring system and that considers the knowledge requirements of autonomic systems. We implemented this architecture as a framework named Panoptes and integrated it to a simple self-protection framework of private Clouds as proof-of-concept. Additionally, we complemented the validation with analytical analyses of the monitoring framework.