A Learning Automata-based Algorithm for Energy-efficient Elastic Optical Networks

Georgia Beletsioti, Georgios Papadimitriou, Petros Nicopolitidis, Emmanouel Varvarigos, Stathis Mavridopoulos


Efficient use of available bandwidth plays an important role in performance enhancement due to the wide penetration of high-bandwidth demanding services. The flexible nature of elastic optical networks (EONs) effectively uses spectral resources for communication by allocating the minimum required bandwidth to customer requirements. Since the energy consumption of such networks scales with the magnitude of bandwidth demand, many studies have addressed the issue of energy wastage in optical networks. Learning Automata are Artificial Intelligence tools that have been used in networking algorithms where adaptivity to the characteristics of the network environment can result in a significant increase in network performance. This work introduces a new adaptive power-aware algorithm, which selectively switches off bandwidth variable optical transponders (BVTs) under low utilization scenarios supporting energy efficiency. A novel algorithm which uses LA technology and significantly reduces the total energy consumption, while maintaining low bandwidth blocking probability (BBP), is proposed. LA mechanism applied in this work, aims to find the best number of BVTs to be switched off so as for the BBP not to be affected. Simulation results are presented, which indicate that the proposed algorithm achieves a power saving of up to 50%, compared to non-adaptive solutions.


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