A Priority-Based Multiobjective Design for Routing, Spectrum, and Network Coding Assignment Problem in Network-Coding-Enabled Elastic Optical Networks

Hai, Dao Thanh; Chau, Le Hai; Hùng, Nguyễn Tấn

doi:10.1109/jsyst.2019.2938590

Cited by 19 publications

(5 citation statements)

References 40 publications

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“…All recurrent neural networks have the form of a chain of neural network modules. In standard RNNs, this repeating module will have a simple structure [39]. Figure 3(a) can be described as; ft, ct, ot, and it is activation functions for hidden, context, output, and input layers, respectively.…”

Section: Deep Learning Neural Network (Dlnn)mentioning

confidence: 99%

Short term residential load forecasting using long short-term memory recurrent neural network

Muneer

Ali

Almaghthawi

et al. 2022

IJECE

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<span>Load forecasting plays an essential role in power system planning. The efficiency and reliability of the whole power system can be increased with proper planning and organization. Residential load forecasting is indispensable due to its increasing role in the smart grid environment. Nowadays, smart meters can be deployed at the residential level for collecting historical data consumption of residents. Although the employment of smart meters ensures large data availability, the inconsistency of load data makes it challenging and taxing to forecast accurately. Therefore, the traditional forecasting techniques may not suffice the purpose. However, a deep learning forecasting network-based long short-term memory (LSTM) is proposed in this paper. The powerful nonlinear mapping capabilities of RNN in time series make it effective along with the higher learning capabilities of long sequences of LSTM. The proposed method is tested and validated through available real-world data sets. A comparison of LSTM is then made with two traditionally available techniques, exponential smoothing and auto-regressive integrated moving average model (ARIMA). Real data from 12 houses over three months is used to evaluate and validate the performance of load forecasts performed using the three mentioned techniques. LSTM model has achieved the best results due to its higher capability of memorizing large data in time series-based predictions.</span>

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Section: Deep Learning Neural Network (Dlnn)mentioning

confidence: 99%

Short term residential load forecasting using long short-term memory recurrent neural network

Muneer

Ali

Almaghthawi

et al. 2022

IJECE

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“…A well-established key feature of fixed-grid optical core networks is their protection and agile restoration when link failures occur. The critical factor that needs to be considered while planning protection schemes is a trade-off between redundant capacity and restoration time [31]. Most popular protection schemes are based on dedicated path protection; however, research studies are exploring the ways to improve the performance of these techniques for fixed-grid and EONs.…”

Section: F Protection and Restorationmentioning

confidence: 99%

“…Most popular protection schemes are based on dedicated path protection; however, research studies are exploring the ways to improve the performance of these techniques for fixed-grid and EONs. Network-coding-based protection [32] is one of such proposed techniques; see for example [31]. With the paradigm shift to SDON, the protection and restoration tasks are performed by a centralized controller, which can perform optimum computations based on the global map of the network.…”

Section: F Protection and Restorationmentioning

confidence: 99%

Dynamic LightPath Allocation in WDM Networks Using an SDN Controller

Qureshi

Braun

2021

IEEE Access

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Core wavelength division multiplexed (WDM) networks are widely used to provide fixed physical connectivity and bandwidth to the logically connected upper electronic layer devices using optical signals. However, growing demands for bandwidth-intensive applications and cloud-based services push optical networks carriers' to provide scalable and flexible services dynamically. Software defined networking (SDN) has the potential to program electronic layers by dynamically controlling and managing network resources using SDN controller applications. SDN's on-demand characteristics combined with the optical circuit-switching can enable optical network service providers to customize their service provisioning dynamically to the user's requirements. They enable fast provision of new services, and minimize under-utilization of resources. In this paper, a model is proposed to bring the dynamic allocation of resources which is a layer 2+ functionality, to the WDM layer using SDN. A middle-ware application based on SDN and OpenFlow for dynamic switching and provisioning of optical service is presented. The application abstracts the optical layer's connectivity, also accounting for the switching constraints. Details of the model's implementation are discussed considering classically used equipment and its performance in terms of CPU and memory utilization, topology emulation time, and latency is evaluated. Finally, the application is tested with a Cisco layer one switch. Performance results show that the latency doubles when increasing the number of fibers of an optical cross connect from 5 to 7 and keeping wavelengths equal to 8, with Clos fabric topology.INDEX TERMS Wavelength division multiplexed network (WDM), Software Defined Networking (SDN), OpenFlow protocol, dynamic path allocation, optical cross connect (OXC).

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“…Aside from spatial multiplexing to improve efficiency and capacity, network coding is being researched in the optical domain for multiplexing and data protection (Hai et al, 2020). Network coded networks allow signal processing to be done at intermediate nodes and routers for example (Yang et al, 2016) presented a multicast-capable RMSA in EON's considering the quality of transmission resulting from physical impairments.…”

Section: Introductionmentioning

confidence: 99%

“…Other works have also shown how all-optical network coding is implemented using optical logic gates in WDM and elastic optic networks. Hai (2017) has applied an all optical XOR gate to protection in transparent WDM networks while Kamal and Mohandespour (2014), Hai et al (2020), and Savva et al (2020) have shown the same applied to EON's for security, protection and multicast communication, and provided solutions to the routing, spectrum and networkcoding assignment problem. This can be beneficial to the alloptical OPM module by reducing the number of intermediate monitoring nodes (for example signal type estimation) since the encoding node re-transmits a linear combination of multiple signals after the XOR operation and for providing protection such that all signals can be acquired by the OPM equipment in case of failure on a single-signal path.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Aided Optical Performance Monitoring Techniques: A Review

2022

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Future communication systems are faced with increased demand for high capacity, dynamic bandwidth, reliability and heterogeneous traffic. To meet these requirements, networks have become more complex and thus require new design methods and monitoring techniques, as they evolve towards becoming autonomous. Machine learning has come to the forefront in recent years as a promising technology to aid in this evolution. Optical fiber communications can already provide the high capacity required for most applications, however, there is a need for increased scalability and adaptability to changing user demands and link conditions. Accurate performance monitoring is an integral part of this transformation. In this paper, we review optical performance monitoring techniques where machine learning algorithms have been applied. Moreover, since many performance monitoring approaches in the optical domain depend on knowledge of the signal type, we also review work for modulation format recognition and bitrate identification. We additionally briefly introduce a neuromorphic approach as an emerging technique that has only recently been applied to this domain.

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A Priority-Based Multiobjective Design for Routing, Spectrum, and Network Coding Assignment Problem in Network-Coding-Enabled Elastic Optical Networks

Cited by 19 publications

References 40 publications

Short term residential load forecasting using long short-term memory recurrent neural network

Short term residential load forecasting using long short-term memory recurrent neural network

Dynamic LightPath Allocation in WDM Networks Using an SDN Controller

Machine Learning-Aided Optical Performance Monitoring Techniques: A Review

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