Routing, Modulation And Spectrum Assignment Research Articles

A routing modulation and spectrum assignment algorithm for semi-filterless elastic optical networks

Development of 5G/F5G technology leads to massive applications accessing to backbone networks, which requires the backbone networks to be upgraded. Semi-filterless elastic optical network (semi-FEON) is a suitable technology to cheaply and gradually upgrade backbone networks. In semi-FEON, routing, modulation and spectrum assignment (RMSA) problem is one of the key issues. In this paper, we study the dynamic RMSA problem in semi-FEON and propose an RMSA algorithm. The algorithm includes three innovations: a K-shortest-subnet-paths (KSSP) algorithm is designed to search candidate paths in semi-FEON, a load-balancing-least-resources (LBLR) policy is introduced to re-sort the candidate paths, and a maximum-occupied-neighbors (MON) rule is proposed to assign spectrum resources to connection requests in semi-FEON. Simulation results show that the proposed KSSP-LBLR-MON algorithm outperforms the existing works in term of bandwidth blocking probability. Concretely, the improvement ratio is greater than 59.98% and 66.64% in German-Net and Henan-Net, respectively.

Link Resource-Aware RMSA Algorithm for Advance Reservation Requests in Semi-Filterless Optical Networks

This paper investigates the routing, modulation, and spectrum assignment (RMSA) problem for advance reservation (AR) requests in semi-filterless optical networks (semi-FONs). A link resource-aware (LRA) algorithm consisting of three parts is proposed. First, in the request scheduling phase, a request sorting strategy based on the flexible window size of the starting time is proposed. This strategy schedules AR requests based on the size of a flexible window with respect to the starting time. Next, in the routing phase, a link resource-aware strategy is proposed. This strategy evenly distributes the requests across the network resources, thereby avoiding request blocking due to excessive resource occupation on a single or few links. In the spectrum assignment phase, a frequency–time block-based metric is employed to measure the availability of each frequency–time block for AR requests. This metric is a sum of two factors: the distance to the block and spectrum boundaries, and the time interval between the request start time and the earliest start time. Our simulation results demonstrate that the proposed LRA algorithm has a lower blocking probability compared to the benchmark algorithms.

A Single–Multi-Path Combinatorial RMSA Algorithm with Least Resource Consumption in Semi-Filterless Optical Networks

Filterless optical networks (FONs) have become a cost-effective solution for optical network deployment due to their low-cost characteristics. However, eliminating active switching elements causes signals to propagate over unintended links, wasting spectral resources. Therefore, semi-filterless optical networks (Semi-FONs) have become a more cost-effective solution. This paper mainly studies the routing, modulation, and spectrum assignment (RMSA) problem in semi-filterless optical networks. It proposes a single–multi-path combination (LR-SMPC) RMSA algorithm with the least resource consumption. The algorithm first obtains the K shortest paths that satisfy the conditions according to the K short path (KSP) algorithm and re-orders the paths according to the resource consumption path re-ordering strategy, selecting the three paths that consume the least resources as the set of candidate paths. Then, based on the single–multi-path combination scheme of the set of candidate paths, the resource consumption of each scheme and the maximum number of available spectrum blocks for each path is calculated, from which the single path or multi-path with the least resource consumption is selected to serve the request. We perform simulation experiments on two network topologies using Poisson traffic models and compare them with existing single-path algorithms (S-P), fixed spectrum assignment granularity algorithms (g = 1), and adaptation spectrum assignment algorithms (g adaptation) to evaluate the performance of the proposed algorithm. The simulation results show that the proposed algorithm exhibits better performance in terms of both blocking rate and spectrum utilization.

A RMSA algorithm for semi-filterless optical networks based on minimum resource consumption and spectrum fragmentation

Semi-filterless optical networks (semi-FONs), a novel passive wide area network (WAN) solution, have attracted considerable attention. This paper investigates the dynamic routing, modulation and spectrum assignment (RMSA) problem in semi-FONs, where requests may propagate on unnecessary links due to the broadcast characteristics of semi-FONs, resulting in wastage of spectrum resources. Moreover, the dynamically setup and torn down of requests can lead to significant spectrum fragments. To address these issues, this paper studies semi-FONs based on two metrics: resource consumption M and fragmentation degree N. Resource consumption M refers to reducing spectrum wastage caused by broadcasting and establishing requests using minimal resources, while fragmentation degree N refers to the situation where the remaining available resources after establishing a request are more concentrated. Based on these metrics, we propose the Minimum Resource Consumption and Fragmentation Degree (Min-RCF) algorithm. We conduct simulation experiments on the German-Net and China’s Henan-Net network topologies, and the results demonstrate that the proposed Min-RCF algorithm achieves lower blocking probability and spectrum waste rate than the compared algorithms.

Minimum link and boundary distance spectrum assignment algorithm based on adaptive modulation scheme in semi-filterless optical networks

Filterless optical networks (FON) are attracting attention because of their low-cost characteristics. However, removing active devices forces the signal to be broadcast on all output ports, wasting a significant amount of spectrum resources. In order to maintain the low-cost advantage and save more spectrum resources, this paper studies the routing, modulation, and spectrum assignment (RMSA) problem in semi-filterless optical networks. For this problem, we propose a spectrum assignment algorithm based on adaptive modulation scheme with minimum link and boundary distance (ML-M-MTBD). The algorithm selects the path that occupies the minimal number of links (ML) for spectrum assignment during the routing process and combines it with a variable adaptive modulation scheme (VAMS) between fiber trees. During the spectrum assignment process, the algorithm divides the frequency spectrum resources into two regions, calculates the distance from the frequency spectrum block to the two boundaries of the spectrum resources to be assigned, and selects the position corresponding to the shorter distance to the boundary for spectrum assignment. We conducted simulations on German-Net and Henan-Net, and compared our algorithm with two existing algorithms, the first-fit algorithm and the best-fit algorithm. The simulation results show that the proposed algorithm has a lower blocking probability.

2-D fragmentation-aware RMSA based on the relative flexibility of advance reservation requests in elastic optical networks

With the advantages of fine granularity spectrum allocation (SA) and large capacity, elastic optical networks (EONs) are considered as the most promising development direction of optical network in the future. The emergence of two-dimensional (2-D) fragmentation during dynamic SA of EONs not only reduces the availability of resources but also increases the blocking probability of requests. In addition, the advance reservation (AR) request has the characteristics of resource reservation and time flexibility, which further complicates the fragmentation issue. To solve the above issues, in this paper, a fragmentation-aware routing, modulation and spectrum assignment (RMSA) algorithm for AR requests is proposed. First, delayed resource allocation is developed for AR requests. Second, a resource allocation sorting strategy based on the relative flexibility of requests is proposed. The strategy makes full use of the time characteristics of AR request to determine the allocation order of requests. In addition, from the perspective of the impact on adjacent resources, a 2-D path resource model is introduced to evaluate the impact of 2-D fragmentation from the perspective of resource matching degree and resource connectivity. Moreover, simulation results show that the proposed algorithm is better than the benchmark algorithm in terms of blocking probability, resource utilization and fragmentation rate.

Artificial Neural Network-Aided Multiclass Service Provisioning and Prioritization in EONs

With the emergence of various applications with diverse transmission requirements, optical networks must support multiclass service provisioning and prioritization. This paper studies serving multiclass requests in elastic optical networks by defining appropriate controlling parameters (CPs), which can be learned by an artificial neural network (ANN). These CPs can be tuned to allocate resources according to the service class priorities. We propose two routing, modulation, and spectrum assignment (RMSA) mechanisms working together to establish data and flow-oriented connections. Two physical layer impairment methods are leveraged to realize connection provisioning by considering the quality of transmission limitations. The RMSA methods are equipped with four CPs to handle the time and frequency resource consumption competition between request classes. An ANN-based regression model is used to determine the value of each CP for preset request classes’ blocking ratios. Exhaustive simulations are performed and analyzed to scrutinize the impact of each CP on the network status. Simulations reveal the effectiveness of ANN decision-making in policy imposition of allocating resources to each request class.

DRL-Based Deadline-Driven Advance Reservation Allocation in EONs for Cloud–Edge Computing

The ongoing roll-out of cloud–edge computing and Internet of Things (IoT) has been simulating the boom of new advance reservation (AR) services, such as bulk-data migration and virtual machine backup, driving the development of substrate elastic optical networks (EONs). These AR requests are initial-delay-insensitive if they are guaranteed to be completed before a predefined deadline. Therefore, the routing, modulation, and spectrum assignment (RMSA) problem is extended to the time-spectrum domain rather than the single spectrum domain. Traditional heuristic RMSA algorithms follow static procedures under handcrafted rules and assumptions, and thus cannot be optimized automatically. To solve this problem, we propose a deep reinforced deadline-driven allocation (DRDA) algorithm. To the best of our knowledge, this work is the first to leverage deep reinforcement learning (DRL) methods to solve the AR resource allocation problem. Moreover, compared with the single experiment scenario of many existing works, the DRDA algorithm is evaluated in both static and dynamic scenarios. Simulation results show that our DRDA algorithm outperforms the other leading algorithms in both static scenario and dynamic scenario.

RMSA algorithms resilient to multiple node failures in dynamic EONs

In Elastic Optical Networks (EONs), the way different service demands are supported in the network is ruled by the Routing, Modulation and Spectrum Assignment (RMSA) algorithm, which decides how the spectrum resources of the optical network are assigned to each service demand. In a dynamic EON, demand requests arrive randomly one at a time and the accepted demands last in the network for a random time duration. So, one important goal of the RMSA algorithm is the efficient use of the spectrum resources to maximize the acceptance probability of future demand requests. On the other hand, multiple failure events are becoming a concern to network operators as such events are becoming more frequent in time. In this work, we consider the case of multiple node failure events caused by malicious attacks against network nodes. In order to obtain RMSA algorithms resilient to such events, a path disaster availability metric was recently proposed which takes into account the probability of each path not being disrupted by an attack. This metric was proposed in the offline variant of the RMSA problem where all demands are assumed to be known at the beginning. Here, we exploit the use of the path disaster availability metric in the RMSA of dynamic EONs. In particular, we propose RMSA algorithms combining the path disaster availability metric with spectrum usage metrics in a dynamic way based on the network load level. The aim is that the efficient use of the resources is relaxed for improved resilience to multiple node failures when the EON is lightly loaded, while it becomes the most important goal when the EON becomes heavily loaded. We present simulation results considering a mix of unicast and anycast services in 3 well-known topologies. The results show that the RMSA algorithms combining the path disaster availability metric with spectrum usage metrics are the best trade-off between spectrum usage efficiency and resilience to multiple node failures.

A Multi-Task-Learning-Based Transfer Deep Reinforcement Learning Design for Autonomic Optical Networks

Deep reinforcement learning (DRL) enables autonomic optical networking by allowing the network control and management systems to self-learn successful networking policies from operational experiences. This paper proposes a transfer learning approach for effective and scalable DRL in optical networks. We first present a modular DRL agent design to facilitate retrieving and transferring relevant knowledge between tasks requiring different dimensions of network state data. In particular, we partition network state data into common states, which contain generic information critical to multiple tasks (e.g., the spectrum utilization on fiber links), and task-specific states that are only used by a specific task (e.g., the utilization of virtual network functions). Separate neural network blocks are employed to process different state data. Based on the modular agent design, a multi-task learning (MTL) aided knowledge transferring scheme is proposed. The proposed scheme trains an MTL agent that can master multiple tasks simultaneously and thus enables to learn and transfer better-generalized knowledge across tasks. We perform case studies on the proposed transfer DRL approach considering two scenarios, namely, knowledge transferring between routing, modulation and spectrum assignment (RMSA) tasks for different networks and knowledge transferring from RMSA tasks to anycast service provisioning tasks. The DRL designs for RMSA and anycast service provisioning, including the state, action, and reward formulations and the training mechanisms, are also elaborated. Performance evaluations under both scenarios show that the proposed approach can effectively expedite the training processes of the target tasks and improve the ultimate service throughput.

Temporal and Spectral 2D Fragmentation-Aware RMSA Algorithm for Advance Reservation Requests in EONs

In this paper, we explore the problem of routing, modulation and spectrum assignment (RMSA) for advance reservation (AR) requests in elastic optical networks (EONs), with the objective to further reduce the blocking probability and improve the spectrum utilization. On the one hand, we propose a minimum resource consumption routing policy (MRC) for leaving more free resources for future requests. On the other hand, we introduce a two-dimensional (2D) path resource model, in which each AR request can be transformed into a rectangle block whose length and width are related to request bandwidth and request duration respectively. Therefore, the spectrum assignment problem of AR requests is simplified to the two-dimensional rectangle packing problem. Furthermore, we design several factors to evaluate the two-dimensional fragmentation caused by a rectangle packing operation, where we consider the fragmentation situation in the rectangle block neighborhood for the first time. Based on these factors, we propose a two-dimensional fragmentation-aware spectrum assignment strategy (2D-FA). Through the combination of MRC and 2D-FA, we propose a novel RMSA algorithm named MRC-2D-FA for dynamically provisioning AR requests in EONs. Simulation results show that MRC-2D-FA can achieve lower bandwidth blocking probability and higher spectrum utilization compared with several well-performed benchmark algorithms.

Virtualization of elastic optical networks and regenerators with traffic grooming

An elastic optical network (EON) plays an important role in transport technology for virtualization of networks. A key aspect of EONs is to establish lightpaths (virtual links) with exactly the amount of spectrum that is needed and with the possibility of grooming, the process of grouping many small traffic flows into larger units, creating a super-lightpath. Grooming eliminates the need for many guard bands between lightpaths and also saves transceivers; however, it often leads to the need to perform optical–electrical–optical conversions to multiple-data-rate optical signals at intermediate nodes. The aim of this paper is to provide a mixed-integer linear programming (MILP) formulation, as well as heuristic and meta-heuristic approaches, for the design of multiple virtual optical networks (VONs) in an elastic optical substrate network with bandwidth-variable lightpaths, modulation format constraints, and virtual elastic regenerator placement. Traffic grooming is allowed inside each VON, and a distance-adaptive modulation format technique is employed to guarantee efficiency in terms of bandwidth for a physical substrate, subject to several virtual topologies. A reduced MILP formulation without grooming capability is also proposed for comparison. The complete MILP formulation jointly solves the virtual topology design, regenerator placement, and grooming problems, as well as the routing, modulation, and spectrum assignment (RMSA) problem. The reduced MILP formulation, heuristics, and meta-heuristic, on the other hand, separate the virtual topology design problem from the RMSA problem. It is shown that the grooming approach can provide good results, since it solves the problem for a complete design when compared to the approach without grooming. Furthermore, heuristic solutions for large networks are proposed, which present good performance (in terms of saving spectrum) for the design with large instances.

Multi-period traffic on elastic optical networks planning: alleviating the capacity crunch

Advances in optical data transmission technology have allowed the current expansion of bandwidth-demanding services over the Internet. Also, the emergence of orthogonal frequency-division multiplexing (OFDM) has opened the possibility of increasing the network spectral efficiency by solving the routing, modulation and spectrum assignment (RMSA) problem. Recently, investigators have examined the effects of multiple demands or multiple virtual topologies when they are requested at different time periods over a single physical substrate. That makes the RMSA harder and with many more instances. Such analysis is required because network traffic does not remain static along time, and the demand can increase considerably as new user services arise. Therefore, planning the network considering a multi-period study becomes essential, since it can prevent a case where demands may exceed the bandwidth capacity and cause request blocking in future periods. In this work, we provide a novel mixed integer linear programming (MILP) formulation to solve the RMSA problem for several t periods of demands. This model can be used not only to find the solutions to minimize the used capacity, but also as an efficient method of network planning, since it can estimate with a single formulation and a single iteration the point of resource exhaustion in each period t. The results are found for a small network, and they show the efficiency of the proposed MILP formulation. We also propose an alternative version of this formulation with predefined paths, which is less computationally demanding. The results of this study are compared to a step-by-step planning, where the strategy is a decomposition method that breaks the previous formulation into t steps. Comparing the results of the two strategies, it can be seen that the single multi-period formulation is a good strategy to solve the problem. By contrast, the step-by-step strategy may require reconfigurations and eventual interruptions in the network, from a step to another one.

RMSA algorithm for malleable-reservation requests in elastic optical networks

Abstract Elastic optical networking is a promising technology for the next generation of optical core networks and the routing, modulation and spectrum assignment (RMSA) problem is one of its key issues. The RMSA problems for immediately reservation (IR) and advance reservation (AR) requests have been widely researched. Malleable reservation (MR) requests are another kind of important traffics in elastic optical networks (EONs). However, the RMSA problem for MR requests has not been well considered. In this paper, we investigate the RMSA problem for MR requests in EONs and propose an algorithm named Min-RRD. The algorithm assigns frequency-time blocks with the minimum sum of three indexes, namely number of used resources ( R used ), number of impacted resources ( R impacted ) and distance to corner ( D corner ), to MR requests. Simulation results show that the proposed algorithm has a lower blocking probability and a smaller delay than the existing algorithm. Hence, it is a well-performed RMSA algorithm.

A Resource-Periodic-Arrangement Strategy for RMSA Problem in Elastic Optical Networks

Elastic optical networks (EONs), which can flexibly assign spectrum resources to connection requests, have become the most promising technology for the next generation of optical core networks. Routing, modulation and spectrum assignment (RMSA) problem is one of the key issues in elastic optical networks. In the RMSA problem, spectrum resources in links are generally divided into frequency slices and connection requests with different capacities can use different numbers of frequency slices to transfer their data; however, this may lead to misalignment of idle frequency slices in links and then result in the increase of blocking probability of connection requests. In this article, we propose a resource-periodic-arrangement (RPA) strategy. The strategy partitions frequency slices into periodically arranged standard frequency blocks and each connection request exactly uses one or more standard frequency blocks. The RPA strategy can effectively reduce the misalignment of frequency slices. Furthermore, an RPA-based RMSA algorithm is designed, in which an enhanced-most-used policy is adopted to choose standard frequency blocks to connection requests. Simulation results show that the RPA-based RMSA algorithm can distinctly reduce blocking probability of connection requests, comparing with existing algorithms.

Building Autonomic Elastic Optical Networks with Deep Reinforcement Learning

Conventional schemes for service provisioning in next-generation elastic optical networks (EONs) rely on rule-based policies that suffer from scalability issues and can lead to poor resource utilization efficiency due to the lack of knowledge about the essential characteristics of EONs (e.g., traffic profiles, physical-layer impairments). This article discusses the application of emerging deep reinforcement learning (DRL) techniques in EONs for enabling an autonomic (self-driving) and cognitive networking framework. This new framework achieves self-learning-based service provisioning capabilities by employing DRL agents to learn policies from dynamic network operations. Such capabilities can remarkably reduce the amount of human effort invested in developing effective service provisioning policies for emerging applications, and thus, can facilitate fast network evolutions. Based on the framework, we first present DeepRMSA, a DRL-based routing, modulation, and spectrum assignment (RMSA) agent for EONs. Then, as today's networks are often composed of multiple autonomous systems, we extend the autonomic networking framework to multi-domain EONs by applying multi-agent DRL (where multiple autonomous DRL agents learn through jointly interacting with their environments). Comparisons of the results from numerical simulations show significant advantages of the proposed framework over the existing rule-based heuristic designs.

A pre-split multi-flow RMSA algorithm in elastic optical networks

Abstract Elastic optical networking is a promising technology for the next generation optical networks, due to its flexibility of spectrum provisioning. Routing, modulation and spectrum assignment (RMSA) problem is one of the key issues in Elastic optical networks. In the problem, constraints of spectrum continuity and spectrum contiguity must be met, which limits the use of spectrum resources. Multi-flow measure, which splits a connection request into multiple flows if the connection request cannot be served as a whole, can relax the spectrum contiguity constraint. The existing multi-flow RMSA algorithms are post-split. In this paper, we propose a pre-split multi-flow RMSA algorithm, in which three innovative measures are adopted, namely partitioning frequency slices into standard blocks, pre-splitting connection requests into flows exactly using the standard blocks, and searching for available standard blocks for the flows with a proposed modified-most-used rule. The measures can make both the occupied and idle frequency slices aligned, so they are expected to lower the blocking probability of connection requests. Simulation results show that the proposed algorithm can get a better performance in term of blocking probability, comparing with three benchmarks.

DeepRMSA: A Deep Reinforcement Learning Framework for Routing, Modulation and Spectrum Assignment in Elastic Optical Networks

This paper proposes DeepRMSA, a deep reinforcement learning framework for routing, modulation and spectrum assignment (RMSA) in elastic optical networks (EONs). DeepRMSA learns the correct online RMSA policies by parameterizing the policies with deep neural networks (DNNs) that can sense complex EON states. The DNNs are trained with experiences of dynamic lightpath provisioning. We first modify the asynchronous advantage actor-critic algorithm and present an episode-based training mechanism for DeepRMSA, namely, DeepRMSA-EP. DeepRMSA-EP divides the dynamic provisioning process into multiple episodes (each containing the servicing of a fixed number of lightpath requests) and performs training by the end of each episode. The optimization target of DeepRMSA-EP at each step of servicing a request is to maximize the cumulative reward within the rest of the episode. Thus, we obviate the need for estimating the rewards related to unknown future states. To overcome the instability issue in the training of DeepRMSA-EP due to the oscillations of cumulative rewards, we further propose a window-based flexible training mechanism, i.e., DeepRMSA-FLX. DeepRMSA-FLX attempts to smooth out the oscillations by defining the optimization scope at each step as a sliding window, and ensuring that the cumulative rewards always include rewards from a fixed number of requests. Evaluations with the two sample topologies show that DeepRMSA-FLX can effectively stabilize the training while achieving blocking probability reductions of more than 20.3% and 14.3%, when compared with the baselines.

A RMSA algorithm for elastic optical network with a tradeoff between consumed resources and distance to boundary

Abstract Elastic optical network (EON) has been a promising technology for the next generation of core transport networks, because it can support flexible spectrum assignment. Routing, modulation and spectrum assignment (RMSA) is one of the key issues in EON. In this paper, two indexes are considered during the spectrum assignment process, number of consumed resources R and distance to boundary I. Minimizing R means using the least resources to establish a connection while minimizing I implies that remainder idle resources are more lumped after a connection is established. Both of them can reduce blocking probability of network. However, optimizations of R and I are sometimes contradictory, so we propose three policies to make a tradeoff between them, which are R : I , I : R and R + I . The R : I policy takes R as the primary objective while the I : R policy sets I as the chief one; the R + I policy takes a linear combination α R + ( 1 - α ) I as the optimizing objective. Simulations are taken on the NSF-Net and UBN topologies, which show that the R + I policy can get the best performance in most cases. We further compare the R + I policy with two existing well-performed algorithms, the First-Last-Fit algorithm and the Block-Assignment algorithm. Simulation results reveal that blocking probability of the R + I policy is far less than that of the comparative objects.

Routing, Modulation, and Spectrum Assignment in Programmable Networks Based on Optical White Boxes

Elastic optical networks (EONs) can help overcome the flexibility challenges imposed by emerging heterogeneous and bandwidth-intensive applications. Among the different solutions for flexible optical nodes, optical white box switches implemented by architecture on demand (AoD) have the capability to dynamically adapt their architecture and module configuration to the switching and processing requirements of the network traffic. Such adaptability allows for unprecedented flexibility in balancing the number of required nodal components in the network, spectral resource usage, and length of the established paths. To investigate these trade-offs and achieve cost-efficient network operation, we formulate the routing, modulation, and spectrum assignment (RMSA) problem in AoD-based EONs and propose three RMSA strategies aimed at optimizing a particular combination of these performance indicators. The strategies rely on a newly proposed internal node configuration matrix that models the structure of optical white box nodes in the network, thus facilitating hardware-aware routing of connection demands. The proposed strategies are evaluated in terms of the number of required modules and the related cost, spectral resource usage, and average path length. Extensive simulation results show that the proposed RMSA strategies can achieve remarkable cost savings by requiring fewer switching modules than the benchmarking approaches, at a favorable trade-off with spectrum usage and path length.