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In this paper, we present Dynamic Cross-layer Network Orchestration (DyCroNO), a dynamic service provisioning and load balancing mechanism for IP over optical networks. DyCroNO comprises of the following components: i) an end-end (E2E) service provisioning and virtual path allocation algorithm, ii) a lightweight dynamic bandwidth adjustment strategy that leverages the extended duration statistics to ensure optimal network utilization and guarantee the quality-of-service (QoS), and iii) a load distribution mechanism to optimize the network load distribution at runtime. As another contribution, we design a real-time deep learning technique to predict the network load distribution. We implemented a Long Short-Term Memory-based (LSTM) method with a sliding window technique to dynamically (at runtime) predict network load distributions at various lead times. Simulations were performed over three topologies: NSFNet, Cost266 and Eurolarge using real-world traffic traces to model the traffic patterns. Results show that our approach lowers the mean link load and total resources significantly while improving the resource utilization when compared to existing approaches. Additionally, our deep learning-based method showed promising results in load distribution prediction with low root mean squared error (RMSE) and ∼90% accuracy. URL: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10144888&isnumber=10144840 

In optical networks, simulation is a cost-efficient and powerful way for network planning and design. It helps researchers and network designers quickly obtain preliminary results on their network performance and easily adjust the design. Unfortunately, most optical simulators are not open-source and there is currently a lack of optical network simulation tools that leverage machine learning techniques for network simulation. Compared to Wavelength Division Multiplexing (WDM) networks, Elastic Optical Networks (EON) use finer channel spacing, a more flexible way of using spectrum resources, thus increasing the network spectrum efficiency. Network resource allocation is a popular research topic in optical networks. In EON, this problem is classified as Routing, Modulation and Spectrum Allocation (RMSA) problem, which aims to allocate sufficient network resources by selecting the optimal modulation format to satisfy a call request. SimEON is an open-source simulation tool exclusively for EON, capable of simulating different EON setup configurations, designing RMSA and regenerator placement/assignment algorithms. It could also be extended with proper modelings to simulate CapEx, OpEx and energy consumption for the network. Deep learning (DL) is a subset of Machine Learning, which employs neural networks, large volumes of data and various algorithms to train a model to solve complex problems. In this paper, we extended the capabilities of SimEON by integrating the DeepRMSA algorithm into the existing simulator. We compared the performance of conventional RMSA and DeepRMSA algorithms and provided a convenient way for users to compare different algorithms’ performance and integrate other machine learning algorithms. 

A centralized Software-defined Network (SDN) controller, due to its nature, faces many issues such as a single point of failure, computational complexity growth, different types of attacks, reliability challenges and scalability concerns. One of the most common fifth generation cyber-attacks is the Distributed Denial of Service (DDoS) attack. Having a single SDN controller can lead to a plethora of issues with respect to latency, computational complexity in the control plane, reachability, and scalability as the network scale increases. To address these issues, state-of-the-art approaches have investigated multiple SDN controllers in the network. The placement of these multiple controllers has drawn more attention in recent studies. In our previous work, we evaluated an Entropy-based technique and a machine learning-based Support Vector Machine (SVM) to detect DDoS using a single SDN controller. In this paper, we extend our previous work to further decrease the impact of the DDoS attacks on the SDN controller. Our new technique called Hierarchical Classic Controllers (HCC) uses SVM and Entropy methods to detect abnormal traffic which can lead to network failures caused by overwhelming a single controller. Determining the number of controllers and their best placement are major contributions in our new method. Our results show that the combination of the above three methods (HCC with SVM and Entropy), in the case of a network with 3 controllers provides greater accuracy and improves the DDoS attack detection rate to 86.12% compared to 79.03% and 81.33% using Entropy-based HCC and SVM-based HCC, respectively. 

In computer networking, simulations are widely used to test and analyse new protocols and ideas. Currently, there are a number of open real testbeds available to test the new protocols. In the EU, for example, there are Fed4Fire testbeds, while in the US, there are POWDER and COSMOS testbeds. Several other countries, including Japan, Brazil, India, and China, have also developed next-generation testbeds. Compared to simulations, these testbeds offer a more realistic way to test protocols and prototypes. In this paper, we examine some available wireless testbeds from the EU and the US, which are part of an open-call EU project under the NGIAtlantic H2020 initiative to conduct Software-Defined Networking (SDN) experiments on intelligent Internet of Things (IoT) networks. Furthermore, the paper presents benchmarking results and failure recovery results from each of the considered testbeds using a variety of wireless network topologies. The paper compares the testbeds based on throughput, latency, jitter, resources available, and failure recovery time, by sending different types of traffic. The results demonstrate the feasibility of performing wireless experiments on different testbeds in the US and the EU. Further, issues faced during experimentation on EU and US testbeds are also reported. 

Network service mesh architectures, by interconnecting cloud clusters, provide access to services across distributed infrastructures. Typically, services are replicated across clusters to ensure resilience. However, end-to-end service performance varies mainly depending on the service loads experienced by individual clusters. Therefore, a key challenge is to optimize end-to-end service performance by routing service requests to clusters with the least service processing/response times. We present a two-phase approach that combines an optimized multi-layer optical routing system with service mesh performance costs to improve end-to-end service performance. Our experimental strategy shows that leveraging a multi-layer architecture in combination with service performance information improves end-to-end performance. We evaluate our approach by testing our strategy on a service mesh layer overlay on a modified continental united states (CONUS) network topology. 

In this article, we enhanced the capability of SIMON (Simulator for Optical Networks) by considering the nonlinear effect of the optical network components and different industry network devices. This is achieved by using an optical route planning library called GNPy (Gaussian Noise model in python) as the calculation model within SIMON. SIMON is implemented in C++ and has mainly been used as an optical network learning tool for studying the performance of wavelength-routed optical networks. It measures the network blocking probability by taking into consideration the optical device characteristics. SIMON can capture the most significant impairments when estimating the Bit-Error Rate (BER) but does not consider fiber dispersion and non-linearities. These impairments can be significant when simulating a large-scale network. GNPy, on the other hand, considers those physical impairments and can give a more accurate signal-to-noise ratio (SNR) estimation validated by real-world measurements. By integrating GNPy with SIMON, we are able to set a minimum SNR threshold, which must be satisfied by any call set up in the network. The integration of SIMON and GNPy makes the resulting simulator not only suitable for academic learning but also valuable for real-world network planning, evaluation, and deployment of optical networks.