Cognitive Model-Based Autonomic Fault Management in SDN
Sung‐Su Kim · 2013
In the past decades, network technologies have rapidly evolved with respect to their conception, coverage, capacities and complexities. Advanced technologies, such as software agents and policy-based network management, have been developed for more efficient network management. However, the development of network management technologies has not kept pace with the rapid evolution of the network technologies. Autonomic network management, which helps the network itself detect, diagnose and repair failures by adaptive configuration changes based on the context, is a solution approach for advanced network management. Autonomic network management is first and foremost, a method to manage complexity. Autonomic network management systems can lead to important business advantages, such as reduction of operational expenditure through task automation. Unfortunately, autonomic network management is still an immature technology that poses many challenges to be solved before successful deployment. In order to be a real autonomic management system, not just a automated system, the system should have enough capacities to adapt itself to changing situations. Autonomic network management systems have relied on control loops to change their behaviors based on observations of situations. Existing control loops provide only a single routine to handle situations, regardless of the nature of problems. Although most urgent problems require speedy solutions, some problems need the best solution notwithstanding its response time. Especially, existing control loops have no idea how to deal with unexpected situations which are not defined by policies. In this thesis, we propose a novel autonomic network management architecture, called CogMan, which is based on a cognitive model for efficient problem resolving and accurate decision making even in unexpected situations. A cognitive control loop of CogMan provides reactive, deliberative, and reflective loops for managing systems based on an analysis of the current status. In order to validate the proposed architecture and the control loop, we apply them to fault management in Software Defined Networks (SDN), as well as legacy networks. We also propose a Fast Flow Setup (FFS) algorithm for fast failure recovery in SDN. Finally, this thesis presents the results of experiments on which managing various failure situations with the proposed architecture and algorithm are evaluated.