The main challenge of autonomic network management lies on modelling and realization of self-* capabilities such as self-configuration, self-optimization, self healing, and self-protection in a way that enables acutely response to ongoing knowledge of the operational changes experienced by deployed systems. We believe that capturing such knowledge requires on-going analysis of emergent system-to-system as well as people-to-system interactions. The knowledge of an autonomic network management system needs to cover network capabilities, environmental constraints (of the system and the network), and business goals and policies. This knowledge will be supplement to reasoning and learning techniques to enhance and evolve this knowledge. Policy-based management, including policy transformation, conflict detection and resolution, provides the governance mechanisms that allow directing the behavior of an autonomic network management system. The 2nd IEEE International Workshop on Modelling Autonomic Communication Environments (MACE 2007) was held as part of Manweek in San José, CA, USA from October 29th to October 30th, 2007. This MACE workshops has promoted specifically the realization of Autonomic Communications Environments (ACE), which we see as service-centric environments exhibiting self-* behavior. Within an ACE, service creation, deployment, and operation are optimized to enable the ACE itself and the deployed services to timely adapt to changing business needs and environmental conditions, almost without human intervention. Realization of this vision represents a significant challenge for the networking and computer science research communities, requiring advances in disparate areas ranging, for example, from software modelling to network security, and from machine learning to performance optimization.weiterlesen