Cognitive Networks: Towards Self-Aware Networks

2.6. Challenges and Future Developments

Autonomic computing in general, and the continued development of FOCALE in particular, face three specific challenges: architectural innovations, incorporation of artificial intelligence, and human–machine interaction.

2.6.1. Architectural Innovations

Applying autonomic principles to networks is much more difficult than applying autonomic principles to non-network components because there is much more inherent interaction between the components of a network, and because network components are much more heterogeneous, offering different technologies and functions that all need to work together. Hence, the elements of FOCALE shown in Figure 2.17 must be able to coordinate each of their one or more activities as well as change their focus as directed by any context and/or algorithmic changes.

This will result in more flexible, service-oriented patterns of interaction, as opposed to traditional top-down, hierarchical systems management. Abstraction is especially important in these types of patterns, as gathering information that is seemingly not directly related to each other requires the ability to represent and reason about needs, capabilities, dependencies and constraints of system components, and how they affect protecting business goals.

The FOCALE team is thus interested in defining a set of architectural components and patterns that can support self-knowledge, self-learning and self-reasoning.

2.6.2. Incorporation of Artificial Intelligence ...