Elastic Optical Networks Research Articles

An Iterative Optimization Approach for Routing, Modulation, and Categorical Spatial Bandwidth Block Allocation to Improve Network Performance for Dynamic Traffic in Elastic Optical Networks

In this work, we have proposed an iterative optimization model for allocating spectral resources in optical networks. The proposed model gives spatial routes and spatial bandwidth allocations in optical networks with variable data-rates, modulation schemes, and optical reach adaptation. We have also proposed an algorithm which allocates continuous and contiguous block of frequency slots (FS) between transponders which forms bandwidth partitions. The primary objective of the bandwidth partition is to reduce spatial fragmentation. The integrated approach includes the routing information from using the optimization model and the categorical spectrum allocation from using the proposed algorithm. The integrated approach has been used for dynamic traffic to improve network performance in terms of bandwidth blocking, link utilization, and fragmentation metrics. It has been shown that the FS utilization (FSU) and link utilization (LU) largely increase in the proposed integrated scheme with 80% LU compared to shortest path first (SPF) routing with LU as low as 20%. Similarly, the standard deviation between FSU in the proposed scheme is approximately 5% compared to 25% in other schemes which shows that the FSU sufficiently increases in the integrated approach.

Multi-Associated Parameters Aggregation-Based Routing and Resources Allocation in Multi-Core Elastic Optical Networks

Space division multiplexing (SDM), as a potential means of enhancing the capacity of optical transmission systems, has attracted widespread attention. However, the adoption of SDM technology has also additionally increased resource dimensions, introduced complex crosstalk, and made it difficult to integrate multi-dimensional fragments. These factors force the transmission constraints to be more complicated. Especially, some factors have a mutual restraint relationship, and excessive consideration of certain factors will cause the deterioration of other ones. Therefore, how to comprehensively consider the associated factors to achieve trade-offs and improve network performance is a problem worthy of study. This paper exploits the advantages of self-organizing feature mapping (SOFM) model to process multi-dimensional data with relevant features. Firstly, multiple constraints will be input into SOFM as mode vectors from the core level. Then, by judging the similarity between the competition layer neuron and the pattern vector, the position of the winning neuron is located, which determines the transmission level of each core. Finally, a routing, core, and spectrum allocation scheme is proposed by preferentially locating the core with higher transmission quality. Along the selected core, the available slots will be classified twice respectively by the number of adjacent cores and crosstalk direction to quickly find the spectrum blocks with relatively small crosstalk. Results indicate the scheme can reduce blocking probability and the resource fragmentation. Further, it can increase the resource utilization within tested network load.

Priority-aware traffic routing and resource allocation mechanism for space-division multiplexing elastic optical networks

In recent years, the introduction of new technologies and applications connected to the Internet has demonstrated the physical incapacity of current optical backbone networks in providing resources soon. One of the main proposals to deal with this problem today is space-division multiplexing elastic optical networks. However, to transport the massive amount of data that is being generated today, these backbone networks should provide efficient resilience mechanisms. We propose a routing algorithm with a protection mechanism that considers traffic priority for elastic optical networks space-division multiplexing. The results obtained demonstrate the proposed algorithm’s high efficiency in establishing resilient high priority connections compared with recently proposed algorithms in the literature.

Online routing and spectrum allocation in elastic optical networks based on dueling Deep Q-network

The study of online routing and spectrum allocation (RSA) problem has assumed increasing importance due to the exponential growth of dynamic traffic with uncertainty in elastic optical networks (EONs). This paper first formulates offline RSA as a mixed-integer linear program (MILP) with the consideration of the time attribute, and then models online RSA by a carefully designed Markov decision process (MDP), including the state, action and reward. To deal with such a complex dynamic optimization problem, a novel algorithm based on the classic deep reinforcement learning (DRL) framework, Deep Q-network (DQN), is developed. For further promoting the training efficiency, a dueling network architecture, an improved ɛ-greedy strategy and a series of parameter adjustments are applied. Simulation results demonstrate the effectiveness of the proposed algorithm and show its superiority in reducing the blocking probability compared with the state of the art, which further exhibits the potential of applying DRL to solve such complex real-time decision-making problems.

MT3A : A Novel Multicast Routing, Spectrum and Modulation-Level Assignment in Elastic Optical Networks

Elastic Optical Networks (EONs) were introduced to eliminate the challenges of Wavelength Division Multiplexing (WDM) networks. EONs can improve resource efficiency compared to WDM networks. The issue of resource allocation in EONs is a major challenge for the optimal use of maximum network capacity for each communication demand. On the other hand, multicasting is defined as the transfer of data from one source to multiple destinations and it is one of the most effective methods based on cost and efficiency in order to provide flow in computer networks. In this article, we present a novel Multicast Two-part Two-path Two-direction Allocation (MT3A) scheme in order to routing, spectrum and modulation-level assignment for dynamic multicast traffic in EONs. With this scheme, each sub-request related to a multicast request is divided into two parts considering the property of Sliceable Bandwidth Variable Transponders (SBVTs) and is set up in two different directions in two different light-paths. By using the MT3A scheme compared to comparative schemes, with increasing the data rate, Bandwidth Blocking Probability (BBP) is reduced, which is very important due to increasing demand for contents with high data rate.

Impairment- and fragmentation-aware dynamic routing, modulation and spectrum allocation in C+L band elastic optical networks using Q-learning

The paper presents a Q-learning based dynamic routing algorithm for C+L band elastic optical networks (EONs) considering fiber impairments such as cross-phase modulation (XPM), self-phase modulation (SPM), amplified spontaneous emission (ASE), and inter-channel stimulated Raman scattering (ISRS). The effect of fragmentation is considered in the Q-learning process in addition to considering constraints related to spectrum continuity, contiguity, and non-overlapping. Three classical spectrum allocation strategies, first-fit, last-fit, and exact-fit are used after the Q-learning routing algorithm. The proposed routing, modulation, and spectrum allocation (RMSA) approach is shown to have a lower blocking probability compared with using K-shortest path routing combined with the three classical spectrum allocation strategies.

A Review and Analysis on Elastic Optical Networks (EONs): Concepts, Recent Developments and Research Challenges

A Review and Analysis on Elastic Optical Networks (EONs): Concepts, Recent Developments and Research Challenges

Static multi-sourced data retrieval in elastic optical networks

Extreme-scale science applications are highly innovative and constantly evolving. They are expected to generate data in the petabyte and exabyte ranges. Erasure coding has been widely adopted for data storage in data center networks, where the data are encoded and stored in multiple locations. Therefore, an efficient data retrieval service is needed to transfer encoded data from selected multiple stored nodes to a single destination. Elastic optical networks are a promising backbone technology for data center communication due to their capability to efficiently and flexibly allocate the huge optical bandwidth to heterogeneous traffic demands. In this paper, the erasure-coded multi-sourced data retrieval routing and scheduling problem is studied for static traffic in elastic optical networks, and the objective is to minimize the total transmission completion time of all the requests. An integer linear programming formulation and low-complexity heuristic are proposed. Furthermore, analytical lower bounds are derived and a meta-heuristic, Tabu Search, is adopted to solve the problem. Numerical results are presented to show the effectiveness of the proposed methods.

Deep Reinforcement Learning-Based RMSA Policy Distillation for Elastic Optical Networks

The reinforcement learning-based routing, modulation, and spectrum assignment has been regarded as an emerging paradigm for resource allocation in the elastic optical networks. One limitation is that the learning process is highly dependent on the training environment, such as the traffic pattern or the optical network topology. Therefore, re-training is required in case of network topology or traffic pattern variations, which consumes a great amount of computation power and time. To ease the requirement of re-training, we propose a policy distillation scheme, which distills knowledge from a well-trained teacher model and then transfers the knowledge to the to-be-trained student model, so that the training of the latter can be accelerated. Specifically, the teacher model is trained for one training environment (e.g., the topology and traffic pattern) and the student model is for another training environment. The simulation results indicate that our proposed method can effectively speed up the training process of the student model, and it even leads to a lower blocking probability, compared with the case that the student model is trained without knowledge distillation.

Delay-aware and resource-efficient service function chain mapping in inter-datacenter elastic optical networks

Network function virtualization (NFV) is emerging as a promising paradigm for network architectures. By migrating network functions from dedicated hardware appliances to software instances running in a virtualized environment, NFV promises to offer a more flexible way to deploy and manage service function chains (SFCs). When deploying these SFCs to users, the network operators require not only the user’s demands (e.g., end-to-end delay) to be satisfied, but require the cost of SFC mapping to be minimized (e.g., resource consumption). To fulfill these two goals, in this paper, we have investigated how to realize the delay-aware and resource-efficient SFC mapping in inter-datacenter elastic optical networks. We first formulate an integer linear programming (ILP) model to solve the problem exactly. The main optimization goal in the ILP model is to jointly minimize resource consumption and end-to-end delay to achieve optimal virtual network function placement. Then, a delay-aware and load-balancing mapping algorithm (DALB-MA) is proposed to obtain a near-optimal solution in a reasonable amount of time. Finally, we evaluate the proposed ILP model and heuristic algorithms via extensive simulations. The results indicate that the proposed ILP model and the DALB-MA outperform the benchmarks in terms of block rate, average cost, number of CPUs used, maximum frequency slot index, and delay margin gain.

Graph wavelets for fault localization in optical mesh networks

• Real-time fault detection and localization mechanism in Elastic Optical Networks (EONs). • Alarm correlation strategies fail in the face of static threshold settings. • Detects and localizes silent network failures. • Multi-resolution analysis based on graph wavelets to address fault detection and localization problems. • Exceeds alarm based engines for fault detection and localization accuracy. EONs (Elastic Optical Networks) form the backbone of web-scale cloud infrastructure. These networks should deal with network faults and restoration in real-time with high accuracy and scalability. Thus, fault management functions to detect and localize such failures, play a critical role in modern day EONs. Network impairments are complex in nature in optical networks such as fiber losses, NLI (Non Linear Impairments) and ASE (Amplified Stimulated Emission). Currently, pre-engineered thresholds are used to detect failures in optical networks. Traditional alarm correlation strategies fail in the face of these static threshold settings. In this work, we introduce a real-time fault detection and localization mechanism, which is agnostic to such pre-engineered threshold settings. We apply multi-resolution analysis based on graph wavelets to address fault detection and localization problems. Thus, we propose to use spectrograms and wavelet coefficients to identify the irregularities in an otherwise smooth graph signal. We present a real-time fault localization analysis based on a novel Wavelet Coefficients Trail (WCT) algorithm to root-cause a client signal failure due to OSNR (Optical Signal to Noise Ratio) degradation. We apply the proposed techniques and algorithms to simulated and actual network data. Our results indicate that the proposed methods can root-cause OSNR degradation to major/minor deviations in received power on optical spans. In conclusion, we present a fault localization framework based on graph wavelets in optical mesh networks.

Shared Protection-Based Virtual Network Embedding Over Elastic Optical Networks

This paper proposes a survivable virtual network embedding model over elastic optical networks considering shared protection against any single substrate node or link failure. A virtual network request is embedded in the substrate network with allocating the primary and backup resources which are node-disjoint. Modulation selection and spectrum allocation with constraints from elastic optical networks are considered in the embedding procedure. We consider the backup computing and transmission resource sharing to reduce the required backup resources. We formulate the proposed model as an integer linear programming problem to minimize the rejection ratio for a given set of virtual network requests. We introduce a greedy-based approach that promotes resource sharing to handle the larger-size problem in a practical time. In order to further improve the performance on the objective value, a deep reinforcement learning-based approach with polynomial time complexity in each episode is developed to solve the problem in multiple stages. We design a dedicated learning agent for each stage considering the problem property. We analyze the usages of different approaches based on performance evaluation. The numerical results show that, compared to a conventional model that adopts dedicated protection, the proposed model with shared protection reduces the rejection ratio by 15% on average in our examined cases.

DRAMA+: Disaster Management With Mitigation Awareness for Translucent Elastic Optical Networks

Elastic optical networks (EONs) have emerged as attractive candidates to satisfy the dramatic growth of demand in 5G and cloud applications. EONs promise to provide high spectrum utilization due to flexibility in resource assignment. In translucent EONs, the spectrum efficiency can be further improved by deploying regenerators. Because of their extremely high flexibility, developing efficient mechanisms and strategies to ensure the survivability of translucent EONs is a challenging problem. In this paper, we consider disaster mitigation in translucent EONs. We propose a new approach to disaster management by introducing the concept of mitigation zone, which identifies a region surrounding the disaster zone wherein lightpaths may be reconfigured with degraded service (with a penalty) in order to improve overall performance. We formulate an integer linear program (ILP) to minimize the penalty due to service degradation after a disaster, and present a heuristic algorithm named Disaster Management Algorithm with Mitigation Awareness and 3R regenerators (DRAMA+). Simulation results demonstrate that the proposed algorithms have a better performance in terms of total penalty and blocking ratio than conventional disaster recovery algorithms.

Dynamic Cross-Layer Restoration to Resolve Packet Layer Outages in FlexE-Over-EONs

As a promising technology, Flex Ethernet (FlexE) helps to realize deterministic and ultra-low latency in metro and transport networks. Meanwhile, previous studies have confirmed the advantages of the symbiosis of FlexE and elastic optical network (EON) (i.e., a FlexE-over-EON) on resource utilization and cost-effectiveness. In this paper, we consider the cross-layer restoration (CLR) in FlexE-over-EONs based on the FlexE-aware architecture. Specifically, we address the situation where an outage happened on one FlexE switch in the packet layer to bring it offline temporarily and then the affected client flows need to be recovered quickly and proactively. Three CLR strategies are first proposed to fully explore the flexibility of FlexE-over-EON for restoring the affected flows. Then, with the strategies, we formulate an integer linear programming (ILP) model and design an auxiliary graph (AG) based algorithm to reroute the affected flows as well as minimize the additional operational expense (OPEX) incurred during the CLR. Extensive simulations verify the effectiveness of our proposed CLR algorithms.

A Multi-Path Routing Method with Traffic Grooming Corresponding to Path Lengths in Elastic Optical Networks

A Multi-Path Routing Method with Traffic Grooming Corresponding to Path Lengths in Elastic Optical Networks

Deep Reinforcement Learning for Provisioning Virtualized Network Function in Inter-Datacenter Elastic Optical Networks

In today’s datacenters (DCs), IT resources virtualization is leveraged to realize Network Function Virtualization (NFV) over general-purpose servers. Meanwhile, most of the service providers (SPs) are planning to use Virtual Network Functions (VNFs) to provide agile and flexible network services. In service provisioning, the VNF selection and mapping greatly affect IT resource utilization in DCs and spectrum resource utilization in optical networks. This paper proposes a Deep Reinforce Learning (DRL)-based algorithm for VNF provisioning. By selecting appropriate VNFs for the service requests, the algorithm intelligently guarantees efficient reusing of deployed VNFs while consuming fewer spectrum resources in inter-DC elastic optical networks (EONs). To facilitate the decision-making of the DRL agent, we first decompose the complex VNF-based service chaining (VNF-SC) into several VNF components (VNFCs), which can be solved one-by-one in turn. Then, a feature matrix-based encoding scheme is designed to represent the set of the VNFCs, the available DCs for the VNFCs, and the VNFC being operated, i.e., the input of neural networks. In addition, considering the complexity and difficulty of the VNF-SC provisioning problem, Double Deep Q Network (DDQN) is introduced in the proposed algorithm. Finally, compared with the benchmark heuristics, the extensive simulation results in different network topologies show that the proposed algorithm can reduce the IT and spectrum resource consumption by at least 9.6% and 1.6%, which proves the effectiveness of the proposed DRL-based VNF provisioning algorithm.

High-Generalizability Reinforcement Learning Based Capacity Optimization in WDM Long-Haul Networks

We propose a fast machine learning assisted capacity optimization in a point-to-point link of an elastic optical network using a reinforcement learning (RL) algorithm. We demonstrate a RL agent trained on idealized parameters in simulation, and compare the results with classical optimization algorithms such as a genetic algorithm and a simple power sweep. Furthermore, we present the high generalization ability of the RL agent under varying link conditions. Experimental results show that for 8 DWDM channels over nine 50km spans of True Wave Classic NZDSF with a span loss of 12 dB the RL agent is able to cope with these unseen conditions, removing the need to optimize for every possible link scenario during training.

A crosstalk- and fragmentation-aware RMSCA strategy in SDM-EONs based on aligned prime-partition of spectrum resources

Space division multiplexing elastic optical networks (SDM-EONs) have become a promising technology for the next generation of optical backbone networks. Routing, modulation, spectrum and core assignment (RMSCA) problem is one of the key issues in SDM-EONs. In the RMSCA problem, spectrum fragmentation and inter-core crosstalk are two main disadvantages. In this paper, a convenient and effective representation method of crosstalk is introduced first; then an aligned-prime-partition (APP) algorithm for the RMSCA problem is proposed by jointly considering crosstalk and fragmentation. The algorithm adopts three innovational measures: partition spectrum resources into prime blocks and align the prime blocks in all cores, relax usage condition of prime blocks to increase generality of prime blocks and, define two indexes to increase continuity and contiguity of spectrum resources. Simulation results show that the proposed APP-RMSCA algorithm has a lower bandwidth blocking probability than the existing well-performed RMSCA algorithm.

Wide-Sense Nonblocking Converting-Converting Networks with Multirate Connections.

In this paper, we consider a two-stage converting-converting (CC) switching network. This structure can be used, for instance, in switches of elastic optical networks (EONs) or in time-division switches. We propose a new routing algorithm based on fixed slot assignment in interstage links. This algorithm, called Fixed Input–interstage Slot Assignment (FISA), reduces the switching network complexity compared to the rearrangeable (RNB) switching networks of the same structure. We derive the wide-sense nonblocking (WNB) conditions for the switching network controlled by this algorithm. The obtained WNB conditions are the same as those of the RNB, but the switching network does not need troublesome and time-consuming rearrangements. When implementing the proposed switching network structure, we can also reduce the number of tunable full-range spectrum converters and replace part of them with fixed spectrum converters, or even use space switches in the first stage. This is especially important when this architecture is applied in EONs.

Deep Reinforcement Learning-Based Routing and Spectrum Assignment of EONs by Exploiting GCN and RNN for Feature Extraction

Deep reinforcement learning (DRL) has been introduced to the routing and spectrum assignment (RSA) of elastic optical networks (EONs) where the RSA policies are learnt during the interaction of a DRL agent with the EON environment. Upon each new traffic arrival, the DRL agent senses the EON state, and makes the RSA decision accordingly. Therefore, the EON state feature extraction is essential for the performance. However, current approaches, mainly based on fully connected neural networks or convolutional neural networks, are unsatisfactory in EON state feature extraction due to the following two reasons. (1) The optical network topology information is not well considered. (2) The path-level features are highly related to the RSA decision, due to the spectrum continuity constraint, while path-level feature extraction has not been well exploited. To overcome the above shortcomings, in this paper, we propose to utilize the Graph Convolutional Neural Network (GCN) and the Recurrent Neural Network (RNN) for the feature extraction of the network topology, and the aggregation of link-level features to path-level features. By doing this, critical RSA-related information can be sensed by the DRL agent to make better actions. Simulation results demonstrate that our proposed method outperforms previous approaches.