Impact of BER in fragmentation-aware routing and spectrum assignment in elastic optical networks

The main weakness of elastic optical networks (EON), under dynamic traffic conditions, stems from spectrum fragmentation. A lot of research efforts have been dedicated during recent years to spectrum defragmentation. In this work, a thorough study about proactive defragmentation is carried out. Effects of the different defragmentation parameters on the EON performance are analyzed, and appropriate values of the defragmentation period, which guarantee suitable network performance while keeping the network control complexity at reasonable values, are obtained by means of extensive simulations. Benefit obtained by applying different defragmentation strategies, in terms of increase in the supported load at a given bandwidth blocking probability, is also reported. Different traffic conditions and network topologies are simulated to assess the validity of the obtained results.

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Proactive defragmentation in elastic optical networks under dynamic load conditions

Hop distance–based bandwidth allocation technique for elastic optical networks

The rapid growth of the technologies, high bandwidth applications, and cloud data centers consume heavy internet service. So, the consumer of the internet expects a high capacity medium for communication. The Elastic Optical Network (EON) provides a flexible and reliable transmission service for consumers. The spectrum fragmentation is a key challenge in EON. In simple terms, unaligned Frequency Slots (FSs) in the network are referred to as fragmented spectrum, while in defragmentation, the available FSs need to be rearranged to create room for the new connection requests. The problem in defragmentation occurs due to the lack of a continuous spectrum and it leads to depreciation in spectrum usage and simultaneously increasing the Blocking Probability (BP) which disrupts the majority of the existing connections in the network. Several techniques and approaches were suggested to reduce the possibility of disruption and reconfiguration in the network while defragmenting the frequency slots. This paper proposes a new algorithm to overcome the drawbacks and improvement in the quality of service of the network. The proposed algorithm holds the approach of proactive and reactive along with the meta-heuristic nature-inspired optimization technique called Jellyfish Search Optimization (JSO). The proposed combination, PR-DF-JFSO outperforms well in terms of spectrum utilization, network efficiency, and quality of service offered when compared to the state-of-the-art spectrum defragmentation algorithms according to the results of experiments done using standard quality metrics. The simulation results show the better utilization of spectrum, reduce the spectrum fragmentation complexity, better bandwidth fragmentation ratio, and overall connection blocking reduced.

/pdf/a-spectrum-defragmentation-algorithm-using-jellyfish-2puyvzepx7.pdf

A Spectrum Defragmentation Algorithm Using Jellyfish Optimization Technique in Elastic Optical Network (EON)

People's demand for high-traffic applications and the need for Internet of Things (IoT) are enormous in smart cities. The amount of data generated by virtual reality, high-definition video, and other IoT applications is growing at an exponential rate that far exceeds our expectations, and the types of data are becoming more diverse. It has become critical to reliably accommodate IoT-based big data with reasonable resource allocation in optical backbone networks for smart cities. For the problem of reliable transmission and efficient resource allocation in optical backbone networks, a novel resource allocation and spectrum defragmentation mechanism for massive IoT traffic is presented in this paper. Firstly, a routing and spectrum allocation algorithm based on the distance-adaptive sharing protection mechanism (DASP) is proposed, to obtain sufficient protection and reduce the spectrum consumption. The DASP algorithm advocates applying different strategies to the resource allocation of the working and protection paths. Then, the protection path spectrum defragmentation algorithm based on OpenFlow is designed to improve spectrum utilization while providing shared protection for traffic demands. The lowest starting slot-index first (LSSF) algorithm is employed to remove and reconstruct the optical paths. Numerical results indicate that the proposal can effectively alleviate spectrum fragmentation and reduce the bandwidth-blocking probability by 44.68% compared with the traditional scheme.

https://www.mdpi.com/1424-8220/19/15/3443/pdf

A Novel Resource Allocation and Spectrum Defragmentation Mechanism for IoT-Based Big Data in Smart Cities.

Network resources are poorly used in Elastic Optical Networks under dynamic traffic conditions. A hybrid scheme where some resources are statically allocated and the remaining ones are devoted to dynamic traffic, is proposed and evaluated.

Using static connections to improve elastic optical networks performance

The main weakness of Elastic Optical Networks (EON) stems from spectrum fragmentation. An analysis of periodic defragmentation in EONs under dynamic traffic conditions is carried out. The effects of different defragmentation parameters on the EON performance are evaluated, and appropriate values, guaranteeing suitable network performance while keeping the network control complexity at a reasonable point, are obtained through simulations. Different network topologies as well as traffic conditions are simulated to assess the validity of the obtained results.

Periodic Defragmentation in Elastic Optical Networks

This work is focused on the characterization of the traffic allocated in Elastic Optical backbone networks when different dynamic traffic profiles are generated. Extensive simulations are done to statistically model the real-time traffic carried by this kind of networks. Well known topologies such as NSFNet and US_Backbone are considered as well as different parameters applied to the traffic generation. Some interesting conclusions are extracted about the properties of the traffic supported by Elastic Optical Networks under dynamic conditions.

Laura Vicario

Papers

Proactive defragmentation in elastic optical networks under dynamic load conditions

Using static connections to improve elastic optical networks performance

Periodic Defragmentation in Elastic Optical Networks

Quantifying dynamic traffic in Elastic Optical networks