Towards self-adaptive network management for a recursive network architecture

Traditionally, network management tasks manually performed by system administrators include monitoring alarms based on collected statistics across many heterogeneous systems, correlating these alarms to identify potential problems or changes to management policies and responding by performing system re-configurations to ensure optimal performance of network services. System administrators have a narrow focus of factors impacting network service provisioning and performance due to the heterogeneity and scale of generated underlying network events. However, self-adaption principles are conceptual approaches for autonomously managing such complex distributed systems. Network management systems that harness such principles can dynamically and autonomously optimise the operation of network services, responding quickly to changes in user requirements and underlying network conditions. In this paper, we present a novel self-adaptive network management framework that takes advantage of a recursive network architecture for a simpler and more comprehensive application of ontologies, semantic web rules and machine learning to automatically adjust network configuration parameters to provide more optimal QoS management of network services. We demonstrate the applicability of the approach using a content distribution network (CDN) operating over such a recursive network architecture.