Space Division Multiplexing Elastic Optical Networks Research Articles

Novel dynamic impairment-aware algorithm for modulation, core, and spectrum assignment in SDM-EONs

Space-division multiplexed elastic optical networks (SDM-EONs) utilizing multi-core fiber (MCF) have been considered to address the growing traffic demand in transport networks. The quality of transmission (QoT) of MCF-based SDM-EONs is affected by inter-core and intra-core physical layer impairments (PLIs). This paper proposes an inter-core crosstalk-aware and intra-core impairment-aware algorithm for modulation, core, and spectrum assignment (CIA-MCSA) in MCF-based SDM-EONs. The CIA-MCSA considers PLI estimation in a dynamic traffic scenario and allocates new lightpaths using strategies to avoid blocking by insufficient QoT of the new lightpath and of already active lightpaths. Using numerical simulation, the performance of the CIA-MCSA is compared with five algorithms proposed by other authors, considering two distinct network topologies, heterogeneous traffic demands, and different levels of inter-core crosstalk. The results show that, when compared with the most competitive of the other algorithms, (i) CIA-MCSA achieves an average reduction of the request blocking probability by at least 33.87%; (ii) CIA-MCSA achieves an average reduction of the bandwidth blocking probability by at least 20.74%; and (iii) CIA-MCSA increases the network spectrum utilization by at least 3.04%.

Efficient embedding of service function chains in space-division multiplexing elastic optical networks

Virtual Network Functions (VNFs) are implemented as software instances on generic servers in Data Centers (DCs). A network service is implemented by an ordered list of VNFs, called Service Function Chain (SFC). SFC embedding decides which DC to place/instantiate a VNF, and how to route traffic through the instantiated VNFs/DCs. Benefits from its high capacity and flexibility, the Space-Division Multiplexing Elastic Optical Networks (SDM-EONs) have been proposed as a promising solution to provide connection service between DCs. In this paper, the problem of SFC embedding in SDM-EONs is studied. We first formulate an Integer Linear Programming (ILP) to minimize the Maximum Index of the Utilized Frequency Slot (MIUFS) for a given set of SFC requests. To achieve scalability, a greedy algorithm is proposed to embed SFCs separately. For each SFC, the lowest cost embedding solution can be found by enumeration would be selected for service embedding. A novel stretch algorithm is then designed to reduce the computing complexity further. The key idea is to place VNFs close to the least-cost path between the source and destination of a request. Three strategies to measure the path cost are evaluated. Simulation results show that our stretch algorithm gives the best overall performance in terms of running time, bandwidth consumption, MIUFS and request blocking probability.

Machine learning aided optimization for balanced resource allocations in SDM-EONs

A fine-grained flexible frequency grid for elastic optical transmission and space division multiplexing in conjunction with spectrally efficient modulations is an excellent solution to the coming capacity crunch. In space division multiplexed elastic optical networks (SDM-EONs), the routing, modulation, core, and spectrum assignment (RMCSA) problem is an important lightpath resource assignment problem. Intercore cross talk (XT) reduces the quality of parallel transmissions on separate cores, and the RMCSA algorithm must ensure that XT requirements are satisfied while optimizing network performance. There is an indirect trade-off between spectrum utilization and XT tolerance; while higher modulations are more spectrum efficient, they are also less tolerant of XT since they permit fewer connections on neighboring cores on the overlapping spectra. Numerous XT-aware RMCSA algorithms restrict the number of litcores, cores on which overlapping spectra are occupied, to guarantee XT constraints are met. In this paper, we present a machine learning (ML) aided threshold optimization strategy that enhances the performance of any RMCSA algorithm for any network model. We show that our strategy applied to a few algorithms from the literature improves the bandwidth blocking probability by up to three orders of magnitude. We also present the RMCSA algorithm called spectrum-wastage-avoidance-based resource allocation (SWARM), which is based on the idea of spectrum wastage due to spectrum requirements and XT constraints. We note that SWARM not only outperforms other RMCSA algorithms, but also its ML-optimized variant outperforms other ML-optimized RMCSA algorithms.

Crosstalk Classification Based on Synthetically Consider Crosstalk and Fragmentation RMCSA in Multi-Core Fiber-Based EONs

Space division multiplexing elastic optical networks (SDM-EONs) based on multi-core fiber (MCF) technologies have attracted widespread attention as a potential means of enhancing large capacity and high flexibility. However, inter-core crosstalk (XT) degrades the quality of transmission. The algorithm for minimizing XT leads to an increase in spectrum fragmentation in the lightpath, which influences the spectrum utilization. Therefore, the question of how to comprehensively consider the two factors and improve the network performance is an issue worthy of study. This paper focuses on maximizing spectrum resource utilization while satisfying the XT constraints. Firstly, we optimize a three-dimensional metric model to evaluate XT and fragmentation more exactly in SDM-EONs. Furthermore, a crosstalk classification (CC) algorithm, which can adjust the XT constraints according to the actual situation of the network, is proposed. Moreover, to match the CC algorithm, we describe the crosstalk and fragmentation in the network and propose a synthetically consider crosstalk and fragmentation (SCCF) algorithm. Finally, simulation results show that the proposed CC-SCCF routing, modulation, core, and spectrum allocation algorithm reduces the XT on existing lightpaths, and also provides a lower probability of blocking and greater spectrum utilization.

Auxiliary graph-based energy-efficient routing resource allocation algorithm in space division multiplexing elastic optical networks

Space division multiplexing elastic optical networks (SDM-EONs) are considered as a efficient solution for increasing the link’s capacity. However, the SDM-EONs brought challenges to be explored, notably the routing, core and spectrum assignment (RCSA) problem. For the real network, another important issue should be considered is the problem of energy consumption. The auxiliary graph (AG) can abstract node devices into elements in it and support various routing policies for different purposes by adjusting the weights of AG edges. Therefore, this paper proposes an auxiliary graph-based energy efficient routing resource allocation algorithm (AG-EERRA) to jointly optimize the energy consumption and RCSA. Each node of the auxiliary graph is abstracted from the devices of physical node. The intra edge weights of nodes set the function associated with the energy consumption model. The inter edge weights of node are composed link length, free frequency slots, modulation efficiency and the value of core crosstalk innovatively. Meanwhile, the impact of inter-core crosstalk on request is reduced by crosstalk avoidance strategy and crosstalk aware strategy. In the proposed auxiliary graph, the shortest weight path is selected to transmit requests that can reduce network energy consumption and blocking probability. Under the high-load network, request splitting is performed for the requests that cannot successfully find a route to improve energy efficiency. Simulation results show that the proposed algorithm can improve the network energy efficiency while reducing request blocking probability compared to comparison algorithms.

Core and spectrum allocation to achieve graceful degradation of inter-core crosstalk with generalized hierarchical core prioritization on space-division multiplexing elastic optical networks

Space-division multiplexing elastic optical networks (SDM-EONs) with multi-core fibers (MCFs) are expected to meet the exponentially increasing demand for traffic. However, it is inevitable that inter-core crosstalk (IC-XT) will hinder efforts to increase capacity. Thus, attempts have been made to leverage SDM-EONs by solving the route, modulation format, core, and spectrum assignment problems, but the effects of IC-XT still need to be mitigated. Existing methods such as XT-Aware first fit and core prioritization methods are insufficient for addressing this issue. In this paper, we propose generalized hierarchical core prioritization for the graceful degradation of IC-XT in core and spectrum allocation. Simulation results establish that our approach mitigates adverse influences from IC-XT and has high flexibility in network environments. This study shows that the dynamic resource allocation method, despite being heuristic and computationally inexpensive, can sufficiently reduce the blocking probabilities in any SDM-EONs with hexagonal MCFs.

Revisiting the modulation format selection problem in crosstalk-aware SDM-EONs

The problem of finding a lightpath to provision a connection request in space-division multiplexing elastic optical networks (SDM-EONs) involves routing, modulation, spectrum and core assignment (RMSCA). Aiming at maximizing spectrum efficiency, the lightpath is always established with the modulation format that gives the highest data rate within the transmission reach and crosstalk (XT) threshold. For SDM-EONs based on multi-core fibers (MCFs), highest rate modulation format leads to lowest XT tolerance/threshold, or more spectrum fragments in adjacent cores. We call this problem XT-fragmentation. XT-fragmentation has been ignored by all existing RMSCA algorithms. In this paper, we first propose a way to measure the potential spectrum loss due to more fragments. Then a new modulation format selection (MFS) algorithm is designed with XF-fragmentation in mind. Simulation results show that RMSCA algorithms with MFS achieves lower request blocking probability than that without MFS.

Solving the Static Resource-Allocation Problem in SDM-EONs via a Node-Type ILP Model.

Space division multiplexing elastic optical networks (SDM-EONs) are one of the most promising network architectures that satisfy the rapidly growing traffic of the internet. However, different from traditional wavelength division multiplexing (WDM)-based networks, the problems of resource allocation become more complicated because SDM-EONs have smaller spectrum granularity and have to consider several novel network resources, such as modulation formats and spatial dimensions. In this work, we propose an integer linear programming (ILP) model without space lane change (SLC) that provides theoretically exact solutions for the problem of routing, modulation format, space, and spectrum assignment (RMSSA). Moreover, to more efficiently solve our model which is difficult to solve directly, we propose three exact algorithms based on model decomposition and evaluate their performance via simulation experiments, and we find that two of our exact algorithms can solve the model effectively in small-scale instances.

Location constrained virtual optical network embedding in space-division multiplexing elastic optical networks

Virtual optical network embedding (VONE) involves two processes, node mapping, and link mapping. Node mapping decides which data center to place the virtual nodes (VNs). Link mapping decides how to route virtual links between placed VNs. In location constrained VONE (LC-VONE), each VN can be mapped only to some substrate nodes. In this paper, we study the problem of LC-VONE in space-division multiplexing elastic optical networks (SDM-EONs). Aiming at load balancing, we formulate the first integer linear programming (ILP) to solve the static LC-VONE problem, where the set of VON requests is given. To achieve scalability, an integrated framework is devised to jointly conduct node and link mappings for each VON request. Three cost metrics are designed for finding the least-cost VON solution. Combining them with the integrated framework, three LC-VONE algorithms are proposed: minimum spectrum consumption (MSC), bottleneck link avoidance (BLA), and lowest traffic load (LTL). By applying them to solve both static and dynamic LC-VONE problems, we found that LTL always outperforms MSC and BLA.

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.

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.

Deadline-Aware Multicast Resource Allocation in SDM-EONs With Fluctuating Delay-Sensitive Traffic

The evolution of generational networks has stimulated the growth of delay-sensitive traffic, destabilizing the already incapacitated existing network infrastructure. Due to time-constrained resource requirements of delay-sensitive traffic, managing end-to-end delays becomes very challenging with limited resources. Therefore, we introduce space-division-multiplexing-elastic-optical-networks (SDM-EONs) and point-to-multi-point (P2MP) transfer mechanisms to increase capacity transmission rates. However, managing resource allocation during SDM-EONs-P2MP transfer requires a multi-constraint optimization (MCO) approach to significantly overcome the severe impact of crosstalk (XT) during a joint optimization of multicast traffic in multi-core fibers (MCFs). To solve this problem, we propose a deadline-aware resource allocation algorithm (PRAA) based on mixed-integer linear programming (MILP) and a heuristic approach while leveraging a delay constraint to support a joint optimization of multicast traffic. During routing, modulation, spectrum, and core allocation (RMSCA), PRAA implements MCO to allocate MCFs to reduce congestion rates by improving sending rates. We simulate experimental results and compare them with two state-of-art algorithms: DaRTree-RMSCA and DST-OM-RMSCA, to evaluate PRAA’s performance during RMSCA. The output results highlight that PRAA-RMSCA outperforms DaRTree-RMSCA and DST-OM-RMSCA during peak load of fluctuating delay-sensitive traffic.

Performance and challenges of Bi-directional resource allocation in multi-core space division multiplexing elastic optical networks

In space division multiplexing-elastic optical networks (SDM-EONs), crosstalk and fragmentation are the significant factors determining overall resource utilization. Crosstalk and fragmentation management spectral resource allocation schemes can enhance the overall resource utilization in SDM-EONs. This paper proposes three crosstalk and fragmentation management bi-directional resource allocation schemes for multi-core SDM-EONs to suppress inter-core crosstalk and network fragmentation. A spatial resource partitioning method is presented to mitigate inter-core crosstalk, and this paper adopts a k-shortest path routing method to suppress fragmentation while spectral resource allocation. To the best of our knowledge, there is no such scheme that handles both crosstalk and fragmentation together while allocating spectral resources in a counter-propagating manner for multi-core fiber (MCF) SDM-EONs. The estimated result shows that the proposed scheme outperforms the traditional co-propagation method and enhances existing resource utilization by reducing blocked requests.

Survivable Routing, Spectrum, Core and Band Assignment in Multi-Band Space Division Multiplexing Elastic Optical Networks

The rapid growth in Internet traffic has contributed to the need to expand network transmission capacity. Multi-band (MB) using existing standard single-mode fibers (SMFs) in the free band is an ideal way to increase the capacity of the fiber. However, the introduction of MB has transferred space division multiplexing - elastic optical networks (SDM-EONs) to multi band-SDM-EONs (MB-SDM <strike xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-</strike> EONs), and changed routing, spectrum and core assignment (RSCA) problem to the routing, spectrum, core and band assignment (RSCBA) problem. At the same time, it introduces a new issue named stimulated Raman scattering (SRS), which affects transmission quality. Although an expanded fiber can carry more services, a large number of services will be interrupted if a link fails, which will bring huge losses. Therefore, it is necessary to propose a reasonable protection strategy to ensure reliable transmission quality. In this paper, the survivable RSCBA problem in MB-SDM-EONs is studied, which copes with link failures and considers SRS in signal-to-noise ratio (SNR) analysis. In order to solve the problem, we propose a band partition protection scheme: working and protection resources are allocated in different frequency bands for increasing SNR in a fault-free network. We formulate this problem and band partition protection scheme as an integer linear programming (ILP) model, which takes into account inter-core crosstalk and SNR analysis. Furthermore, a heuristic algorithm based on genetic algorithm is proposed for large-scale networks. We evaluate the performance of the proposed approaches through experimental simulation. The results demonstrate that both proposed approaches are effective in finding the optimal solutions.

Novel fragmentation-aware algorithms in space Division Multiplexing Elastic Optical Networks

Space Division Multiplexing Elastic Optical Networks (SDM-EONs) has been presented to provide flexibility and high speed transmission to increase network capacity and optimal use of network resources. Dynamic establishment and release of connections causes fragmentation in EONs, thus increasing blocking probability as a result of increased fragmentation. In this paper, we present three novel algorithms that focus on how to allocate resources in SDM-EONs under Multi-Core Fiber (MCF) in order to reduce blocking probability by reducing bandwidth fragmentation. The Core Classification Aware of Fragmentation (CCAF) is a method based on the core classification mechanism that uses the concept of demand splitting and a cost function to determine the appropriate spectral space for a new request. The Fragmentation Aware Spectrum and Core Assignment (FASCA-without priority) is a routing and spectrum allocation method that uses a cost function to specify the appropriate spectral space to improve spectrum efficiency and reduce fragmentation. The Fragmentation Aware Spectrum and Core Assignment (FASCA-with priority) is a routing and spectrum allocation method that considers priority of requests to improve spectrum efficiency and reduce fragmentation. Simulation results show that the proposed CCAF algorithm can achieve better performance compared with the FASCA-without priority and FASCA-with priority in terms of blocking probability and spectrum utilization. Moreover, FASCA-without priority can provide better performance than FASCA-with priority.

Multi-channel multi-task optical performance monitoring based multi-input multi-output deep learning and transfer learning for SDM

We propose a transfer learning (TL) simplified multi-input multi-output multi-task convolutional neural network (MIMO MT-CNN) to realize the optical performance monitoring (OPM) for space division multiplexing (SDM) fiber transmission systems. TL simplified MIMO MT-CNN is used for NRZ, RZ and PAM4 signal joint monitoring including modulation format identification (MFI) and optical signal-to-noise ratio (OSNR) estimation. Thanks to the TL technologies, the parameters used in single mode fiber transmission is transferred to SDM to reduce the required training data and epochs. The experiment has been demonstrated with MIMO MT-CNN for seven cores fiber systems. The results show that the accuracy of MFI can reach 100% for all signals. For NRZ, RZ, and PAM4, the root-mean-square errors (RMSE) of OSNR estimation are less than 0.6 dB, respectively. In addition, by implementing TL from simulation to experiment, the required training samples and epochs are reduced by 48% and 50%, respectively. Due to its low cost and being easy to implement, the proposed OPM solution has the potential for next-generation SDM fiber transmission systems. The model can realize short training time and multi-task multi-channel OPM for high-capacity SDM elastic optical networks, simultaneously.

Resource Allocation Scheme Based on Complete Planning Process for Immediate and Advance Reservation in SDM-EONs

Immediate reservation (IR) and advanced reservation (AR) become two common traffic models for future space division multiplexing-elastic optical networks (SDM-EONs). However, it is crucial to reduce conflicts between different types of requests and to accommodate more requests with limited resources. In this paper, we propose a resource allocation scheme based on complete planning process (RA-CPP) for IR and AR in SDM-EONs, including request provisioning, resource planning, passive adjustment, and active adjustment. In the request provisioning, the starting time of requests is considered and the earlier transmitted requests have higher resource selection priority. Next, the spectrum blocks with minimal resource spacing between adjacent requests (RS-AR) are planned for requests to maximize the free resources of the network in the resource planning. In addition, for the new request that would be blocked, passive adjustment begins. After planning reserved resources for the new request, conflicting AR requests are re-provisioned. Finally, the active adjustment re-optimizes the resources by increasing the tightness of spectrum resources. Simulation results show that the proposed RA-CPP scheme has greater advantages in blocking probability and spectrum utilization compared with the benchmark schemes.

Improving the Performance of SDM-EON Through Demand Prioritization: A Comprehensive Analysis

This paper studies the impact of demand-prioritization on Space-Division Multiplexing Elastic Optical Networks (SDM-EON). For this purpose, we solve the static Routing, Modulation Level, Spatial Mode, and Spectrum Assignment (RMLSSA) problem using 34 different explainable demand-prioritization strategies. Although previous works have applied heuristics or meta-heuristics to perform demand-prioritization, they have not focused on identifying the best prioritization strategies, their inner operation, and the implications behind their good performance by thorough profiling and impact analysis. We focus on a comprehensive analysis identifying the best explainable strategies to sort network demands in SDM-EON, considering the physical-layer impairments found in optical communications. Also, we show that simply using the common shortest path routing might lead to higher resource requirements. Extensive simulation results show that up to 8.33% capacity savings can be achieved on average by balanced routing, up to a 16.69% capacity savings can be achieved using the best performing demand-prioritization strategy compared to the worst-performing ones, the most used demand-prioritization strategy in the literature (serving demands with higher bandwidth requirements first) is not the best-performing one but the one sorting based on the path lengths, and using double-criteria strategies to break ties is key for a good performance. These results are relevant showing that a good combination of routing and demand-prioritization heuristics impact significantly on network performance. Additionally, they increase the understanding about the inner workings of good heuristics, a valuable knowledge when network settings forbid using more computationally complex approaches.

Survival Multipath Energy-Aware Resource Allocation in SDM-EONs During Fluctuating Traffic

Energy consumption efficiency during resource allocation is crucial for large scale networks. The evolution of space division multiplexing elastic optical networks (SDM-EONs) has presented tremendous spatial dimensions over single-mode networks to manage transmission of largetraffic volumes, during peak load of the fluctuating traffic. However, due to large capacitytransmission rates, the resource allocation challenges have led to incremental energy consumption. This is due to the impact of crosstalk (XT), which occurs between active adjacent cores, during core switching, and increasing service disruptions caused by rampant link failures. To address the above issues, this article proposes a distance-adaptive energy-aware resource allocation (DERA) algorithm using a survival multipath scheme. The DERA algorithm implements the concept of the survival multipath scheme with Spectrum Compactness (SC) to minimize dynamic XT effects in SDM-EONs, during resource allocation. The objective is to improve spectral efficiency to save energy consumption, when the load of the fluctuating traffic peaks. During Routing, Modulation, Spectrum, and Core Allocation (RMSCA), the DERA algorithm can efficiently allocate resources in a survival multipath scheme. The performance of the DERA-RMSCA is compared with the two Multipath Protection (MPP) schemes, PRIME-RMSCA, and CMDE-RMSCA. The results demonstrate that DERA-RMSCA can achieve a better performance compared with PRIME-RMSCA, and CMDE-RMSCA.

Protected Resource Allocation in Space Division Multiplexing-Elastic Optical Networks with Fluctuating Traffic

In this paper, we introduce a Protected Routing, Modulation, Spectrum, and Core Allocation (RMSCA) algorithms with fluctuating traffic in Space Division Multiplexing-Elastic Optical Networks (SDM-EONs). This RMSCA algorithm is called Traffic Awareness Cross-talk Interference Avoidance (TACIA-RMSCA). We investigate the efficiency of efficient allocation of spectrum resources to minimize the impact of crosstalk (XT), and blocking probability. We first assess the impact inter-core crosstalk in Multi-Core Fiber (MCF) with the peak load of the fluctuating traffic. This is usually caused by signal leaks generated by the adjacent cores. It can occur whenever the transmitted optical signals overlap their spectrum segments. We design the Triangular Iterative Core Assignment (TICA) strategy to overcome XT, and to minimize the blocking probability. We evaluate the performance of TACIA-RMSCA with the benchmark algorithm (GAK-RMSCA). Experimental results demonstrate that the proposed algorithm can achieve promising performance with the peak load of the fluctuating traffic.