Event Detection and Correlation for Network Environments

Autonomic communication has the aim of supporting fast-evolving network technologies and services, and of reducing the burden in managing complex and dynamic network environments. Networks with desirable self-* properties should be more adaptable to changing conditions and would enable greater flexibility and functional scalability. A necessary condition for realising these benefits is a heightened level of network awareness; this requires not merely the capacity to monitor the system and network state, but also the ability to characterise the operational environment and its dynamic shifts. As argued in the literature, patterns of change can be detected through cross-correlation of monitored or sensory inputs expressed in events. The profiling of the temporal ordering and other relationships encoded in these patterns can provide contextual information suitable for reasoning and adaptation tasks. In this paper, we present the design framework and initial evaluation of an Information Sensing system that aims to enable awareness through an integrated event detection-correlation mechanism. In the context of autonomic networks, it offers a more lightweight solution than traditional active database-oriented event systems, and it has better performance than log-post analysis processing; its design also provides for a distributed detection facility.