Dynamic realization of miscellaneous profile services in elastic optical networks using spectrum partitioning

TL;DR

This paper introduces probabilistic spectrum partitioning and profile-based routing techniques for elastic optical networks, enabling dynamic, flexible bandwidth services with high request accommodation success rates.

Contribution

It proposes two novel spectrum assignment schemes utilizing probabilistic partitioning to improve request realization and reduce blocking in elastic optical networks.

Findings

Request realization probability exceeds 0.993 for loads under 400 Erlang.

Proposed algorithms effectively meet service profile constraints.

Enhanced spectrum utilization and request acceptance rates.

Abstract

Optical backbone networks are required to be highly dynamic in supporting requests with flexible bandwidth granularities to cope with the demands of new broadband wireless and fixed access networks. To provide this flexibility, services are offered by taking requested bandwidth profile into consideration, instead of assigning a fixed amount of bandwidth to each request. New techniques are developed for the resource management of the elastic optical networks to realize services with a specified bandwidth profile, consisting of minimum, average, and maximum required number of spectrum slots, in addition to holding time. In this work, two new schemes are proposed to realize such services, exploiting a probabilistic spectrum partitioning approach. This new probabilistic spectrum partitioning scheme is devised to enhance the chance of accommodating requests and consequently lower request blocking probability. It enforces different probabilities to contributing spectrum partitions in a certain service realization. Taking advantage of this probabilistic spectrum partitioning and a profile-based routing, we introduce two multistage spectrum assignment methods to make a certain lightpath meet the requested service profile constraints, considering the time-weighted average of the assigned spectrum slots. The results indicate that our algorithms can successfully realize the requests with the probability of 0.993 for the offered loads less than 400 erlang.