Agent-Based Simulation of Behavioral Anticipation in Computer Networks: A Comparative Study of Anticipatory Fault Management

Abstract

Network fault management is concerned with the detection, isolation and correction of anomalous conditions that occur in a computer network. Present state of art in fault management classifies existing methodologies into two main categories: reactive rule based approaches and intelligent monitoring systems. We explore the concept of anticipatory behavior to develop an intelligent agent-based network management model, which uses an anticipatory agent to proactively detect occurrence of faults using a predictive Bayesian model pertaining to network performance. To analyze the effectiveness of the anticipatory technique, we compare it with alarm correlation and rule-based reactive fault management strategies. Results of the comparative analysis are presented to demonstrate the potential of the anticipatory technique in detecting network anomalies. Our findings indicate that the anticipatory technique improves network performance significantly better than the reactive techniques. We furthermore describe a methodology for adaptive restructuring of the network based on the simulated annealing process. We observe that adaptive restructuring gives significantly better performance under the reactive rule-based fault-management technique as compared to the anticipatory strategy.