Survivable Elastic Optical Networks Research Articles

Multi-objective optimization of asymmetric bit rate partitioning for multipath protection in elastic optical networks

In survivable elastic optical networks, multi-path protection combined with traffic squeezing has gained attention. Link-disjoint multipath routing (LD-MPR) and bandwidth squeezing protection (BSP) when applied to the routing, modulation level and spectrum assignment problem are efficient strategies to address the excessive bandwidth demanded by protected services, spectral fragmentation and link-load balancing. LD-MPR enables the division of service transmission bit rate into independent flows, whereas BSP tolerates traffic reduction under link failure. An open issue in the literature is how to efficiently divide demanded traffic among the link-disjoint routes under dynamic-traffic and BSP, while complying the service level agreement (SLA). We propose in this paper a multi-objective-optimization genetic algorithm that defines how service transmission bit rate should be partitioned among the candidate link-disjoint routes. A customized partitioning definition is made for each source–destination node pair in the network, aiming the simultaneous minimization of network blocking probability and average squeezing transmission bit rate experienced by the services during single-link failure. We also propose, a fixed-alternate routing using groups of disjoint paths (FARgdp). Complex dynamic-traffic network scenarios that simultaneously consider BSP, LD-MPR and FARgdp are addressed.

Center fit spectrum allocation policy for time-varying traffic in survivable elastic optical networks

The bandwidth required of time-varying traffic changes with time. How to allocate bandwidth and choose survivable transmission lightpath for time-varying traffic poses a great challenge to the performance of elastic optical networks. In this paper, a survivable time-varying traffic spectrum allocation based on center fit spectrum allocation with quasi-center spectrum expansion/contraction (STTSA-CFSA-QCSEC) algorithm is proposed for time-varying traffic in survivable elastic optical networks. Specifically, we focus on how to allocate varying bandwidth for time varying traffic in a survivable elastic optical network. Considering the characteristics of time-varying traffic and the direction of bandwidth variation for time-varying traffic, we propose a spectrum allocation policy different from that of traditional fixed-bandwidth requests. At the same time, for reducing the overhead of protecting bandwidth, a survivable single/dual lightpath hybrid policy is designed. Simulation results show that the proposed algorithm can effectively reduce the bandwidth blocking probability and improve spectrum utilization of elastic optical networks.

Robust RMSA design for shared backup path protection based EON against nonlinear impairments

We focus on quality of transmission (QoT) guaranteed routing, modulation, and spectrum allocation (RMSA) in shared backup path protection (SBPP) based elastic optical networks (EONs) in the presence of nonlinear impairments. Allocating a suitable modulation format (MF) to ensure end-to-end QoT (E2E QoT) in a survivable EON offers critical challenges because each single link failure results in the failure of different network connections. As a consequence, different working and backup path allocations exist in the network for different failures, which lead to different nonlinear interference (NLI) power on any frequency slots (FSs). This demands a robust RMSA design to decide the suitable MF for each FS to ensure the E2E QoT under two scenarios: any single link failure and no failure. For this purpose, we propose a robust SBPP-NLI aware (SBPP-NLIA) algorithm to ensure E2E QoT with a multi-objective function that enables the joint sharing of backup FSs as well as transceivers and minimizes the overall network fragmentation. In the process, we introduce robust bitloading in our SBPP-NLIA to allocate different MFs for different FSs where we also perform optimal MF allocation for each FS of every connection. Next, we ensure the QoT of existing connections while establishing each new connection without reserving any margin. To achieve this with less complexity, we propose a recursive method to calculate the NLI for existing connections. Simulation results demonstrate that our proposed SBPP-NLIA performs 10.01% and 14.19% better in terms of, respectively, the bandwidth blocking probability and fragmentation compared to a full-load assumption scheme with an 85 Tb/s traffic load.

Proactive connection recovery strategy with recovery time constraint for survivable elastic optical networks

This paper presents a halfway signaling exchange shared path protection (HSE-SPP) on the backup route for a fast connection recovery strategy. In the proposed HSE-SPP, a pre-assigned intermediate node on the backup route is chosen for signaling exchange. When connection fails, source and destination nodes simultaneously generate backup connection setup messages to the pre-assigned intermediate node on the reserved backup route. At the intermediate node, signaling process occurs, and acknowledgment is generated for data transmission to the respective end nodes. Consequently, connection recovery time by applying HSE-SPP becomes very low. Simulations are performed for network parameters and results are verified with existing strategies. The average recovery time (RT), bandwidth blocking probability (BBP), bandwidth provisioning ratio (BPR), and resource overbuild (RO) ratio of HSE-SPP for ARPANET is 13.54ms, 0.18, 3.02, 0.55, and for dedicated path protection (DPP) are 13.20ms, 0.56, 6.30, 3.75 and for shared path protection (SPP) 22.19ms, 0.22, 3.23, 0.70 respectively. Similarly, average RT, BBP, BPR and RO of HSE-SPP for COST239 are 8.33ms, 0.04, 1.64,0.26, and for DPP 4.23, 0.47, 3.50, 2.04, and for SPP 11.81, 0.08, 1.66, 0.27 respectively. Hence, results of the proposed strategy are better in terms of RT, BBP, BPR, and RO ratio.

Defragmentation based on route partitioning in 1 + 1 protected elastic optical networks

In survivable elastic optical networks (EONs), spectrum fragmentation is one of the major obstacles to efficiently use spectrum resources. Efficient defragmentation approaches are indispensable to reduce spectrum fragmentation in survivable EONs, where both full 1 + 1 protected and quasi 1 + 1 protected services exist. The full 1 + 1 protected service offers full reliability without causing any traffic disturbance, where the backup path is not allowed to be reallocated, i.e., 1 + 1 path protection is always maintained. Whereas, the quasi 1 + 1 protected service compromises the reliability if any failure occurs on a primary path while its corresponding backup path is being reallocated. When full 1 + 1 protected and quasi 1 + 1 lightpaths are mixed in EONs, the network operators need to consider them to achieve thorough defragmentation carefully. This paper presents and investigates defragmentation based on route partitioning intending to reduce the spectrum fragmentation and blocking probability in 1 + 1 protected EONs, where both full 1 + 1 protected and quasi 1 + 1 protected services exist. A defragmentation scheme that focuses on minimizing the retuning interference on the full 1 + 1 protected lightpaths is introduced; the retuning interference occurs when a full 1 + 1 protected lightpath cannot be retuned to fill in a gap left by an expired lightpath due to the interference of another lightpath that prevents it from being moved further. We define a route partitioning optimization problem to minimize the interference on full 1 + 1 lightpaths in 1 + 1 protected EONs considering both full 1 + 1 protected and quasi 1 + 1 protected lightpaths. We formulate the problem as an integer linear programming (ILP) problem. The decision version of route partitioning problem in 1 + 1 protected EONs is proved as an NP-complete problem. We develop a heuristic algorithm for the case where the ILP is not tractable. We provide an online defragmentation algorithm for dynamic traffic. The effectiveness of the introduced defragmentation scheme with route partitioning is demonstrated through a simulation study.

Leveraging double-agent-based deep reinforcement learning to global optimization of elastic optical networks with enhanced survivability.

As the services in elastic optical networks (EONs) are bandwidth-intensive, unpredictable and dynamic, increasing network factors are emerging to affect the performance of network survivable planning and operation from network capacity to efficiency. Most of the traditional protection and restoration approaches may become before long inefficient due to the improvement of a particular network performance metric always at the expense of others. We argue that it would be more beneficial for comprehensive optimization of network performance to consider main network metrics jointly. Moreover, the highly dynamic features of EONs call for the new generation of machine learning-based solutions that are flexible and adaptable to cope with the dynamic nature of services to perform analytics. In this paper, we investigate the problem of global optimization of network performance under survivable EON environment. Specifically, a criterion, named the whole network cost-effectiveness value with survivability (WCES), is defined to measure the overall network performance by balancing the interaction among main network metrics. Then we propose a deep reinforcement learning (DRL) -based heuristic with the objective of improving overall network performance, in which two agents are utilized to provide working and protection schemes converging toward better survivable routing, modulation level and spectrum assignment (S-RMLSA) policies. Numerical results show that the proposed criterion can efficiently measure the overall network performance, and the double-agent DRL-based heuristic can greatly improve WCES while ensuring the network survivability and paying the acceptable extra consumption of request blocking probability.

On the efficient optimization of unicast, anycast and multicast flows in survivable elastic optical networks

The paper investigates efficient allocation of three types of flows (unicast, anycast, multicast) in elastic optical network with dedicated path protection. We model the problem as an integer linear programming and propose efficient solution methods: greedy randomized algorithm (GRA) and column generation (CG)-based approach implemented in two versions, which differ in the method used to find a final problem solution for the selected columns. Then, we evaluate in detail methods efficiency with respect to four reference algorithms. What is more, for CG-based methods we use five different algorithms to find initial sets of columns and, by these means, evaluate how the quality of these methods influences the efficiency of CG-based approaches. The simulation results show that the proposed GRA significantly outperforms reference greedy methods and finds very good solutions (the average gap to optimal result was not higher than 13.4%). The CG-based methods allow to further improve results and obtain solutions very close to optimal ones (for the best CG-based version, the highest average gap to optimal result was 2.1%). Moreover, the results show that the traffic pattern influences the algorithms performance. The methods perform the best for scenarios with high amount of multicast volume while high anycast volume brings the most difficult problem instances. Eventually, the study reveals that the efficiency of CG-based methods depends strongly on the quality of initial columns as well as on the method used to find final solution for the selected columns.

Routing, Spectrum and Modulation Level Assignment, and Scheduling in Survivable Elastic Optical Networks Supporting Multi-Class Traffic

As elastic optical networks (EONs) are expected to convey high bitrate connections, a failure in these networks causes vast data losses. Thus, addressing the problem of survivability in EONs is of great importance. In this paper, survivability of an EON with multiclass traffic is investigated, where each traffic type needs specific protection mechanism and scheduling strategy. In particular, a connection request may demand for dedicated path protection, shared backup path protection, or restoration mechanism. Besides, it can tolerate a pre-determined delay and be scheduled at future time slots, referred to as advance reservation, or must be served immediately without any delay, referred to as immediate reservation. Accordingly, we formulate an integer linear programming (ILP) to solve the routing, spectrum and modulation level assignment, and scheduling problems in both static and dynamic operation scenarios. In addition, to reduce the complexity of the ILP formulation in large scale networks, we propose a number of heuristic algorithms, in which two and three methods are considered for path selection and resource allocation, respectively. We evaluate the proposed heuristic algorithms in a small scale network by comparing their performance with the ILP formulation, then we apply them in a realistic large scale network. We compare all algorithms in terms of blocking probability, initial delay, and spectrum efficiency. Our results reveal that by using load-aware path selection and first-fit frequency-slice and time-slot assignment strategies the best compromise between blocking probability and initial delay is obtained.

Artificial Bee Colony for optimization of cloud-ready and survivable elastic optical networks

In this paper we study important and weakly-investigated optimization problem imposed by the fast telecommunication networks development. The problem concerns routing of three types of flows (unicast, anycast, multicast) in survivable Elastic Optical Network (EON). Three types of flows allow to efficiently support cloud-related services while network survivability, provided by dedicated path protection, addresses problem of a single link failure. Since the problem is very challenging, we propose efficient and large-scale optimization method based on the Artificial Bee Colony (ABC) framework. Then, we perform extensive numerical experiments to evaluate ABC performance with respect to the reference methods. The results prove high efficiency of the ABC-based algorithm, which finds very good solutions (average gap to optimal result was less than 10% for all studied cases) and outperforms the reference methods for the majority of problem instances.

Traffic grooming with survivability and power-efficiency in software defined elastic optical networks

In the current work, we address the power-efficiency issue in a survivable elastic optical network (EON). Initially, to obtain solutions for the grooming with survivability routing and spectrum allocation problem in a software defined EON, we formulate an integer linear program (ILP) model which reduces both, the transponder numbers needed and the maximum amount of frequency slots which are occupied. Next, we propose the novel route protection with grooming and shared backup (RPwGSB) heuristic algorithm which considers four grooming cases and aims at providing adequate protection and reduced consumption of the resources (i.e., spectrum). We conduct extensive simulations to evaluate the consumed power by the optical elements which belong to the data plane. The obtained simulation results through both, the formulated ILP model and the proposed RPwGSB algorithm demonstrate a similar trend and show that with the introduction of grooming, more power savings can be obtained. Further, the results also reveal that compared to the traditional route protection with shared backup scheme which does not involve any grooming, the proposed RPwGSB algorithm achieves better performance in terms of blocking probability, total port cost and power-efficiency, simultaneously demonstrating significant spectrum savings.

Two metaheuristics for routing and spectrum allocation in cloud-ready survivable elastic optical networks

Abstract Contemporary society relies on fast and reliable transfer of information, leading to ever-increasing size of transmitted data that stimulates the development of new algorithms for network management and optimization. This paper covers problem of allocating three types of network flows (unicast, anycast, multicast) in elastic optical network that implements dedicated path protection. The problem answers some users expectations such as efficient realization of cloud- and content-related services (by means of three flow types) and provision of network survivability. The problem is modelled as an integer linear programming with respect to two optimization criteria related to the spectrum usage. Next, solution algorithms are proposed which include, among others, two metaheuristics based on particle swarm optimization and tabu search. The results of simulations evaluate efficiency of the algorithms and prove that the proposed tabu search approach significantly outperforms other methods.

A column generation technique for routing and spectrum allocation in cloud–ready survivable elastic optical networks

Abstract Driven by increasing user requirements and expectations, the fast development of telecommunications networks brings new challenging optimization problems. One of them is routing and spectrum allocation (RSA) of three types of network flows (unicast, anycast, multicast) in elastic optical networks (EONs) implementing dedicated path protection (DPP). In the paper, we model this problem as integer linear programming (ILP) and we introduce two new optimization approaches—a dedicated heuristic algorithm and a column generation (CG)-based method. Then, relying on extensive simulations, we compare algorithm performance with reference methods and evaluate CG efficiency in detail. The results show that the proposed CG method significantly outperforms reference algorithms and achieves results very close to optimal ones (the average distance to optimal results was at most 2.1%).

Leveraging Spectrum Sharing and Defragmentation to $p$ -Cycle Design in Elastic Optical Networks

In this letter, a new protection technique, namely Spectrum Shared pre-configured cycles (SS- $p$ -cycles), is proposed for survivable elastic optical networks. SS- $p$ -cycles permit to cut spectrum usage and spectrum fragmentation ratio (SFR) by leveraging special spectrum sharing among multiple $p$ -cycles that have common link(s). To compute SS- $p$ -cycles, we first formulate an integer linear programming model to obtain the optimal solution in small instance, and then develop a time-efficient heuristic algorithm to solve large-scale instances. Simulation results have demonstrated that the proposed SS- $p$ -cycles outperform conventional $p$ -cycles without spectrum sharing in terms of spectrum usage and SFR.

Shared path protection through reconstructing sharable bandwidth based on spectrum segmentation for elastic optical networks

Abstract In order to address the problems of spectrum fragmentation and low sharing degree of spectrum resources in survivable elastic optical networks, an improved algorithm, called shared path protection by reconstructing sharable bandwidth based on spectrum segmentation (SPP-RSB-SS), is proposed in the paper. In the SPP-RSB-SS algorithm, for reducing the number of spectrum fragmentations and improving the success rate of spectrum allocation, the whole spectrum resource is partitioned into several spectrum segments. And each spectrum segment is allocated to the requests with the same bandwidth requirement in priority. Meanwhile, the protection path with higher spectrum sharing degree is selected through optimizing the link cost function and reconstructing sharable bandwidth. Hence, the protection path can maximize the sharable spectrum usage among multiple protection paths. The simulation results indicate that the SPP-RSB-SS algorithm can increase the sharing degree of protection spectrum effectively. Furthermore, the SPP-RSB-SS algorithm can enhance the spectrum utilization, and reduce the bandwidth blocking probability significantly.

Modeling and optimization of data center location and routing and spectrum allocation in survivable elastic optical networks

In this paper, we focus on the problem of locating data centers (DCs) and routing traffic demands in survivable elastic optical network (EON). We consider and compare two different modeling and optimization approaches: joint optimization of both issues at the same time (problem DC-RSA) and decomposition method, which divides problem into two subsequent subproblems of DC location and demands routing with a given DC locations. For both approaches we formulate integer linear programming (ILP) models and propose heuristic algorithms to solve related problem instances. In case of the decomposition method, we propose to use topology-based (that follows directly from the network graph structure) and demographic-economical (characteristics of the cities that correspond to network nodes) data to define DC location policies. We define seven different DC location polices, which can be classified as topology-based, demographic-economical, and hybrid (use both groups of information). Next, we perform extensive numerical experiments on realistic network topologies, in order to evaluate efficiency of the proposed optimization methods and compare different optimization approaches in terms of the computational complexity, spectrum usage, and survivability provisioning in case of a random large-scale disaster. The results of our investigation show that the joint optimization problem (DC-RSA) is much more complex than the decomposition approach. However, the corresponding results bring better network performance according to the spectrum usage and provide good level of network survivability at the same time. Regarding different DC location policies applied with decomposition method, the investigation proves that DC location policy has a crucial influence on the network performance (in terms of the spectrum usage and survivability provisioning), especially when the number of available DCs is relatively small. According to the results, topology-based DC location policies significantly outperform demographic-economical methods in terms of both—spectrum usage and survivability provisioning.

ILP modelling and joint optimization of anycast and unicast traffic in survivable elastic optical networks

In this paper, an optimization problem in elastic optical networks (EONs) is considered. The problem concerns routing and spectrum allocation with joint optimization of anycast and unicast demands protected by dedicated optical paths. A new integer linear programming (ILP) problem formulation is presented, which aims at overcoming scalability problems of the ILP model considered so far. Therefore, the ILP formulations are discussed and compared according to the number of variables, constraints and processing time.

Survivable elastic optical networks: survey and perspective (invited)

Elastic optical networks (EONs) have been receiving extensive attention because of their inherent flexibility and the efficiency with which they allocate fiber-bandwidth. For an EON, survivability is of crucial importance because of the very high bandwidth that it carries on each optical channel. In this article, we review the current state of the art of survivable EONs which reviews the literature to summarize the following aspects: (a) spectrum resource sharing among backup lightpaths, (b) sharing of high-speed optical transponders, (c) effect of spectrum conversion, (d) bandwidth squeezed restoration (BSR), (e) joint restoration by multiple sub-band optical channels, (f) multi-layer survivability, and (g) energy efficiency. Apart from a summary on the current research status, we also discuss open research issues which are important to survivable EONs from the perspectives of (a) impact of spectrum conversion, (b) impact of elastic optical transponder configuration, (c) impact of physical layer impairments and limitations, (d) protection-path-based spectrum defragmentation, and (e) network availability.

On Spectrum Efficient Failure-Independent Path Protection <italic>p</italic>-Cycle Design in Elastic Optical Networks

High spectrum efficiency and fast restoration speed are highly desired for survivable elastic optical networks (EONs). In this paper, we take the advantages of failure-independent path-protecting preconfigured cycles (FIPP p -cycles) and investigate how to realize spectrum efficient resilience design with them. We first study the problem of offline service provisioning with FIPP p -cycles. We formulate an integer linear programming model and prove that the problem is $\mathcal{NP}$ hard. Then, several time-efficient heuristics are designed for FIPP p -cycle formulation and related routing, modulation format, and spectrum assignment. Extensive simulations on offline provisioning verify that the heuristics can obtain near-optimal solutions. Next, we consider online service provisioning with FIPP p -cycles in dynamic EONs. In order to overcome the decrease of protection efficiency during dynamic network operation, we propose a p -cycle reconfiguration scheme to reoptimize protection structures on-the-fly. Simulation results demonstrate that the proposed algorithms can improve spectrum efficiency and reduce bandwidth blocking probability simultaneously.

Routing and spectrum allocation algorithms for elastic optical networks with dedicated path protection

Elastic optical network (EON) architectures have been recently proposed as a candidate solution for provisioning of both huge bandwidth and flexible connections in next generation optical networks. In this paper, we focus on survivable EON scenarios and, in particular, we address an offline problem of routing and spectrum allocation (RSA) with dedicated path protection (DPP) in EON. We formulate RSA/DPP as an Integer Linear Programming (ILP) problem. Since RSA is a difficult problem itself, we propose to apply a metaheuristic approach to provide near-optimal solutions to RSA/DPP. Namely, we develop a Tabu Search-based algorithm (TS), and a hybrid Adaptive Frequency Assignment-TS (AFA/TS) algorithm. We investigate the efficiency of the algorithms for a set of network and DPP scenarios and we show that the proposed algorithms outperform other reference algorithms. Eventually, we present some comparative results for different path protection scenarios.

AN EVOLUTIONARY ALGORITHM APPROACH FOR DEDICATED PATH PROTECTION PROBLEM IN ELASTIC OPTICAL NETWORKS

Elastic optical network (EON) architectures are a very promising solution for next generation optical networks. In EON, a basic problem in network design and operation is the problem of routing and spectrum allocation (RSA). In this work, we focus on RSA in a survivable EON with dedicated path protection (DPP) consideration. Because RSA is a difficult problem itself, we develop an evolutionary algorithm (EA) with the aim to support the search for optimal solutions. We investigate the effectiveness of the algorithm for a set of survivable network scenarios. Evaluation results show that the proposed algorithm outperforms reference algorithms from the literature.