Resource learning aware algorithm for route prediction and resources allocation in elastic optical networks with network analytics

We have proposed two algorithms in this paper for dynamic traffic in elastic optical networks (EON). Algorithm 1 considers the spatial access blocking probability metric (SABPM) during dynamic operation and route traffic to a path with lower SABPM. Therefore, it considers a path with more contiguous blocks to support heterogeneous bandwidths (BW) and modulation schemes. Algorithm 1 keeps a balance of fragmentation on available routes. Algorithm 2 makes routing decision for dynamic traffic based on the maximum idle slots as well as minimum SABPM. Therefore, it selects a less fragmented path with more number of available contiguous blocks to support heterogeneous BW. Algorithm 2 balances the path utilization and avoids over utilization of congested route. We have evaluated the performance of both algorithms in terms of spatial external fragmentation, contiguity ratio, contiguous aligned spectrum ratio, SABPM, and BW blocking probability (BwBP). Python with seaborn, pandas, and other libraries are used for network analytics. Results show that resource utilization improves with the proposed algorithms. It has been shown that algorithm 2 and algorithm 1 achieve 27% and 18% traffic gains respectively over the alternate routing. • Two dynamic traffic management algorithms are proposed for Elastic Optical Networks. • Algorithm 1 selects routes with lower SABPM and more contiguous idle FS blocks. • Algorithm 2 routes dynamic traffic based on maximum idle slots and minimum SABPM. • Evaluation using metrics like bandwidth blocking probability and fragmentation. • Algorithm 2 achieves a 27% traffic gain; Algorithm 1 achieves 18%.