Cognitive Management and Control of Optical Networks in Dynamic Environments

Emerging network traffic requires a more agile network management and control system to deal with the dynamic network environments than today’s networks. We propose the use of cognitive techniques for the fast and adaptive management of future optical networks. As a first approximation, we model our expected traffic arrivals as a multi-state Markov process and categorize different network traffic environments by the length of the network coherence time. For the traffic with moderate and short coherence times, the stopping-trial estimator still responses to the traffic changes with a short detection time as long as the inter-arrival times of traffic transactions are independent. The algorithm provides no prejudice on the exact network traffic distribution avoiding having to sense and estimate detailed arrival traffic statistics. To further deal with the fast-changing traffic, we model the transient convergent behaviors of network traffic drift as a result of traffic transition rate changes and validate the feasibility and utility of the traffic prediction. When the network traffic rate changes quickly, our sequential maximum likelihood estimator will capture the traffic trend with a small number of arrivals and provide fast reconfiguration, which is very important for maintaining quality of service during large traffic shifts.