Network providers attach a significant focus to fault-management. Indeed, availability and quality of service are highly important parameters in the competition between networks operators. Tthe involvement of human in the decision making process and the analyzing a huge amount of alarms and information, as well as the reactive nature of fault management mechanisms, do not allow the required reactivity for optimal management of incidents. This thesis focuses on proactive mechanisms which anticipate failures to improve the effectiveness of their management. Indeed, the failures are often preceded by alarms or symptomatic behaviors. Implementation, in equipment, of autonomous components capable of continuously analyzing the network health would enable to provide a real-time risk of failure information, required to deploy new proactive self-healing mechanisms. The first part of this thesis is devoted to the definition of architectural components necessary for the introduction of proactive self-healing functions. Then, in a second step, we study and analyze in detail three self-healing mechanisms exploiting a proactive risk-level of failure information. The first mechanism is designed to accelerate the convergence of link-state routing protocols by adjusting the frequency of sending failure detection messages function of the risk-level. The second mechanism dynamically tunes routing metrics in order to divert traffic flows from risky equipment and to minimize the failure incidence on traffic. Finally, the last proposition is dedicated to the recovery mechanisms of GMPLS protocol by dynamically adapting the resources consumption of recovery to the involved risks
