Continuous and Parallel Optimization of Dynamic Bandwidth Scheduling in WDM Networks

Many scientific and Grid applications require high-speed circuits of guaranteed bandwidth for scheduled transfers. Offline optimization of dynamic scheduled bandwidth demands is an efficient way of finding the near-optimal solution to the bandwidth scheduling problem. In this paper, we propose a continuous and parallel optimization method to address the dynamic and deterministic bandwidth scheduling problem in next generation wavelength-division multiplexing (WDM) networks. In this method a greedy algorithm and genetic algorithm are run in parallel in separate threads and both of them take the Dynamic Scheduled Bandwidth Demand (D-SBD) as their input. The user gets his response only from the greedy algorithm and hence he will get a deterministic answer in a short amount of time. The genetic algorithm takes as one of its inputs the output of the greedy algorithm and does the optimization of the D-SBDs with minimizing blocking probability as its fitness function. The greedy algorithm copies the optimized reservation database of the genetic algorithm at regular intervals. The user submitting a D-SBD request is unaware of the optimization done by the genetic algorithm. This method is evaluated using both trace-driven simulation of real network traffic from the DOE ESnet network and stochastic traffic in ESnet network topology and a 24 node network topology. We also compare our approach with an earlier proposed method called re-optimization at blocking. Adding the genetic algorithm improves the performance of the network (in terms of blocking probability) compared to using only the greedy approach or the re-optimization at blocking method.