AI-driven Network Function Virtualization (NFV) service chaining is emerging as a key enabler for automation in edge-to-cloud infrastructures. However, existing standards and orchestration frameworks lack mechanisms for fairness, coordination, and dynamic resource sharing across distributed AI agents. This paper proposes a hierarchical architecture that integrates adaptive agent placement and fairness-aware scheduling for AI-based NFV coordination. At its core, the Multi-Agent Dynamic Bandwidth Environment (MADBE) framework leverages deep reinforcement learning to enable agents to collaboratively allocate shared bandwidth while meeting latency and throughput constraints. Experimental results demonstrate that MADBE significantly improves convergence speed, reduces conflict rates, and maintains bandwidth utilization near the 90% threshold, outperforming state-of-the-art baselines. Moreover, MADBE sustains near-zero violation rates for service-level constraints, even under dynamic and heterogeneous workloads. These results highlight the potential of fairness-aware, multi-agent coordination in next-generation 5G/6G networks and industrial automation environments.

Efficient vehicular edge computing networks (VECNs) integration in 6 G networks is essential for low-latency connected autonomous vehicle (CAV) services, but optimizing path planning and virtual network function (VNF) allocation under dynamic conditions remains challenging. Existing methods overlook their interdependence, leading to suboptimal outcomes. This paper proposes a joint optimization framework, modeled as a Markov decision process (MDP) and solved using Rainbow-deep Q-Network (DQN). We evaluate three training strategies-Sequential, Multi-agent-time-specific with network-load-prediction, and Federated learning-finding that Federated achieves the highest success rate and minimal system failures, enhancing network utilization and service continuity.

In industrial multi-access edge computing (MEC), novel collaborative systems involving multiple automated guided vehicles (AGVs) require the execution of both critical tasks and computational tasks, such as AI-based collision avoidance. However, previous research focused mainly on scheduling process-related tasks and overlooked computational tasks. Moreover, scheduling tasks between AGVs in collaborative systems poses an integer problem, which is challenging to solve in polynomial time, highlighting the need for computationally efficient algorithms. This paper proposes a deep reinforcement learning (DRL)-based approach to multi-task scheduling (DRL-MTS) in multi-AGV systems. It involves dynamically applying a catalog of DRL models, each tailored to different numbers of AGVs. Evaluation results demonstrate that the proposed inter-AGV DRL-MTS strategy closely approaches the optimal solution, reaching up to 96% task completion compared to a 98% of the optimal solution, while significantly reducing decision times. Moreover, the training time for these models has been reduced threefold using datasets from existing optimization solvers, and transfer learning has further cut training times by up to 51%.

Resource distribution among diverse administrative domains, network operators, and geographical locations across the edge-to-cloud continuum requires suitable communication and management and orchestration (MANO) mechanisms among orchestration domains. In multi-access edge computing (MEC) environments, efficient application lifecycle management and system federation are essential for scalability, optimal resource utilization, and ensuring service continuity and reliability. Existing orchestration solutions, typically designed for centralized cloud architectures, often fall short in accommodating application delay requirements and in managing the dynamic and distributed nature of MEC resources effectively. This paper introduces a cloud-native, platform-agnostic MEC Orchestrator (MEO) with enhancements over the ETSI MEC architecture, albeit aligned with GSMA and ETSI MEC federation standards, that supports cross-platform MANO and resource controllability. Federation is supported through a new MEO-to-MEO interface that enables application migration across MEC systems. Experimental results demonstrate a 95% instantiation success rate for instantiation, overperforming the baseline Kubernetes scheduler, and 93.3% for migration requests within federated MEC systems in high request volume scenarios.

The paper explores customized services for Connected Autonomous Vehicles (CAVs) in Beyond 5G (B5G) and 6G networks. It proposes an optimal VNF placement solution in Edge Computing (EC)-enabled heterogeneous networks for CAVs. The solution leverages Deep Reinforcement Learning (DRL) to allocate computing resources based on demand and network conditions intelligently. The performance evaluation compares a value-based approach, two policy-based approaches, and an iterative Integer Linear Programming (ILP) algorithm. Simulation results demonstrate that our proposed value-based DRL solution outperforms the ILP algorithm in decision-making response time and performs near-optimal in terms of cost per route and total hops per route.

This paper presents a Ph.D. thesis proposal for a novel solution in optimizing the placement of Connected Autonomous Vehicles (CAVs) Virtual Network Functions (VNFs) requests in Edge Computing (EC) resources. Our Federated Deep Reinforcement Learning (FDRL) proposal will be designed to improve computation efficiency while minimizing service rejections and maximizing resource utilization, and ensuring the least costly path for CAVs. This approach will also be privacy-preserving, ensuring sensitive data remains secure and enables reliable, low-latency communication between CAVs, EC nodes, and the federated server. By utilizing distributed learning capabilities, FDRL allows multiple vehicles to learn from their local experience and make collective decisions, improving network systems performance.

Edge to Cloud Continuum is a concept that integrates cloud computing and cellular networks that has been gaining popularity due to its potential to provide a seamless user experience and address the challenges of managing complex multi-domain networks involving massive IoT devices. Enabling intelligence in the Edge to Cloud Continuum can further enhance its capabilities, offering benefits such as reduced latency, improved scalability, enhanced resource utilization, and increased context awareness. This paper provides insights into the opportunities and challenges of enabling intelligence in Edge to Cloud Continuum, highlighting the potential of this technology. This study presents a comprehensive review of the existing literature on enabling intelligence in Edge to Cloud Continuum, to reach the research questions that will construct the PhD. Various tools and technologies that can be used to integrate intelligence into the Edge to Cloud Continuum system were explored and analyzed. In addition, this study provides a detailed work plan for the upcoming months of the project.

Services and use cases in 5G and beyond networks are characterized by strict requirements such as ultra-low latency, increased capacity, and high user mobility. Moreover, these networks must be capable of satisfying these ambitious demands as well as anticipating and adapting to dynamically changing conditions in a quick and feasible manner. This study deals with the problem of determining the best time to readjust the user plane function (UPF) placement and session mapping configuration to avoid quality of service (QoS) degradation in the system due to user mobility. To this aim, we rely on machine learning (ML) techniques to anticipate poor QoS events and decide whether a reconfiguration procedure is required based on a pre-established QoS tolerance threshold. Specifically, an ML-based framework, called intelligent scheduling of the reconfiguration (ISR), is proposed to automate the reconfiguration process. This framework applies supervised ML methods, either regressors or classifiers, to predict the QoS values/status at a given time horizon. The simulation experiments revealed the proposed mechanism’s superiority compared to the established scheduling baseline. The ISR solution could not only keep the system QoS under desired values most of the time but also reduce the number of readjustment events by at least 50% compared to the baselines.

Advanced technologies like network function virtualization (NFV) and multi-access edge computing (MEC) have been used to build flexible, highly programmable, and autonomously manageable infrastructures close to the end-users, at the edge of the network. In this vein, the use of single-board computers (SBCs) in commodity clusters has gained attention to deploy virtual network functions (VNFs) due to their low cost, low energy consumption, and easy programmability. This paper deals with the problem of deploying VNFs in a multi-cluster system formed by this kind of node which is characterized by limited computational and battery capacities. Additionally, existing platforms to orchestrate and manage VNFs do not consider energy levels during their placement decisions, and therefore, they are not optimized for energy-constrained environments. In this regard, this study proposes an intelligent controller as a global allocation mechanism based on deep reinforcement learning (DRL), specifically on deep Q-network (DQN). The conceived mechanism optimizes energy consumption in SBCs by selecting the most suitable nodes across several clusters to deploy event requests in terms of nodes’ resources and events’ demands. A comparison with available allocation algorithms revealed that our solution required 28% fewer resource costs and reduced 35% the energy consumption in the clusters’ computing nodes while maintaining high levels of acceptance ratio.

Network function virtualization (NFV) and multi-access edge computing (MEC) have become two crucial pillars in developing 5G and beyond networks. NFV promises cost-saving and fast revenue generation through dynamic instantiation and the scaling of virtual network functions (VNFs) according to time-varying service demands. Additionally, MEC provides considerable reductions in network response time and backhaul traffic since network functions and server applications can be deployed close to users. Nevertheless, the placement and chaining of VNFs at the network edge is challenging due to numerous aspects and attendant trade-offs. This paper addresses the problem of dynamic user plane function placement and chaining reconfiguration (UPCR) in a MEC environment to cope with user mobility while guaranteeing cost reductions and acceptable quality of service (QoS). The problem is formalized as a multi-objective integer linear programming model to minimize multiple cost components involved in the UPCR procedure. We propose a heuristic algorithm called dynamic priority and cautious UPCR (DPC-UPCR) to reduce the solution time complexity. Additionally, we devise a scheduler mechanism based on optimal stopping theory to determine the best reconfiguration time according to instantaneous values of latency violations and a pre-established QoS threshold. Our detailed simulation results evidence the efficiency of the proposed approaches. Specifically, the DPC-UPCR provides near-optimal solutions, within 15% of the optimum in the worst case, in significantly shorter times than the mathematical model. Moreover, the proposed scheduling method outperforms two scheduler baseline solutions regarding the number of reconfiguration events and QoS levels.

This study attempts to solve the placement and chaining problem of 5G User Plane Functions (UPFs) in a Multi-access Edge Computing (MEC) ecosystem. The problem is formalized as a multi-objective Integer Linear Programming (ILP) model targeted at optimizing provisioning costs and quality of service. Our model takes into account several aspects of the system such as UPF-specific considerations, the Service Function Chain (SFC) requests topology (single and multiple branches), Virtual Network Function (VNF) order constraints, service demands, and physical network capacities. Since the formulated problem is NP-hard, two heuristic solutions are devised to enhance solution efficiency. Specifically, an algorithm called Priority and Cautious-UPF Placement and Chaining (PC-UPC) and a simulated annealing (SA) meta-heuristic are proposed. Through extensive simulation experiments, we evaluated the performance of the proposed solutions. The results revealed that our solutions outperformed the baselines (i.e., two greedy-based heuristics and a variant of the classical SA) and that we had obtained nearly optimal solutions with significant reductions in running time. Moreover, the PC-UPC algorithm can effectively avoid SFC rejections and improve provisioning costs by considering session requirements, current network conditions, and the effects of VNF mapping decisions. Additionally, the proposed SA approach incorporates several mechanisms (e.g., variable Markov chain length and restart–stop) that allow the improvement of not only the quality of the solutions but also their computation time.

Multi-access Edge Computing (MEC) is a key technology in the road to 5G and beyond networks. Significant reductions in both latency and backhaul traffic can be achieved by placing server applications, and network functions at the network edge. However, this implies new challenges for their dynamic placement and management. In this paper, we tackle the problem of dynamic placement reconfiguration of 5G User Plane Functions (UPFs) in a MEC ecosystem to adapt to changes in user locations while ensuring QoS and network operator expenditures reduction. In this vein, an Integer Linear Programming (ILP) solution is proposed to determine the optimal UPF placement configuration (e.g., number of UPFs and user-UPF mapping) by considering several cost components along with service requirements. Moreover, a scheduling technique based on Optimal Stopping Theory (OST) is presented to decide the optimal reconfiguration time according to instantaneous values of latency violations and established QoS thresholds. Extensive simulation results demonstrate their effectiveness, achieving significant improvements in metrics such as number of re-computation events, reconfiguration costs, and number of latency violations over time.

Software-defined networks (SDNs) have caused a paradigm shift in communication networks as they enable network programmability using either centralized or distributed controllers. With the development of the industry and society, new verticals have emerged, such as Industry 4.0, cooperative sensing and augmented reality. These verticals require network robustness and availability, which forces the use of distributed domains to improve network scalability and resilience. To this aim, this paper proposes a new solution to distribute SDN domains by using Data Distribution Services (DDS). The DDS allows the exchange of network information, synchronization among controllers and auto-discovery. Moreover, it increases the control plane robustness, an important characteristic in 5G networks (e.g., if a controller fails, its resources and devices can be managed by other controllers in a short amount of time as they already know this information). To verify the effectiveness of the DDS, we design a testbed by integrating the DDS in SDN controllers and deploying these controllers in different regions of Spain. The communication among the controllers was evaluated in terms of latency and overhead.

Most Distributed Denial of Service (DDoS) detection and mitigation strategies for Internet of Things (IoT) are based on a remote cloud server or purpose-built middlebox executing complex intrusion detection methods, that impose stringent scalability and performance requirements on the IoT due to the vast amounts of traffic and devices to be handled. In this paper, we present an edge-based detection scheme using BPFabric, a high-speed, programmable data-plane switch architecture, and lightweight network functions to execute upstream anomaly detection. The proposed detection scheme ensures fast detection of DDoS attacks originated from IoT devices, while guaranteeing minimum resource usage and processing overhead. Our solution was compared against two widespread coarse-grained detection techniques, showing detection delays under 5ms, an overall accuracy of 93 - 95% and a bandwidth overhead of less than 1%.

The Internet of Things is one of the hottest topics in communications today, with current revenues of $151B, around 7 billion connected devices, and an unprecedented growth expected for next years. A massive number of sensors and actuators are expected to emerge, requiring new wireless technologies that can extend their battery life and can cover large areas. LoRaWAN is one of the most outstanding technologies which fulfill these demands, attracting the attention of both academia and industry. In this paper, the design of a LoRaWAN testbed to support critical situations, such as emergency scenarios or natural disasters, is proposed. This self-healing LoRaWAN network architecture will provide resilience when part of the equipment in the core network may become faulty. This resilience is achieved by virtualizing and properly orchestrating the different network entities. Different options have been designed and implemented as real prototypes. Based on our performance evaluation, we claim that the usage of microservice orchestration with several replicas of the LoRaWAN network entities and a load balancer produces an almost seamless recovery which makes it a proper solution to recover after a system crash caused by any catastrophic event.

Under 5G use case scenarios latency is a main challenge that must be addressed, since mission critical environments are mostly delay sensitive. To achieve this goal, the service infrastructure placement optimization is needed in the interest of minimizing the delays in the service access layer. To solve this problem, this paper mathematically models the placement problem in a Fog Computing/NFV environment as a Mixed-Integer Linear Programming problem and proposes a heuristic-based solution considering 5G mobile network requirements. As a practical result, an application was developed to achieve usability and flexibility while ensuring operational applicability of the proposed methods.

Achieving energy efficiency has recently become an essential aim of networking research due to the ever increasing power consumption and CO2emissions generated by large data networks. For this problem, the emerging paradigm of Software-Defined Networks (SDN) can be seen as an attractive solution. In these networks an energy-aware routing model could be easily implemented leveraging the control and data plane separation. This paper addresses the problem of optimizing the power consumption in SDN using an energy-aware traffic engineering approach that minimizes the number of links that can be used to satisfy a given traffic demand. Different from previous works, we focus on optimizing energy consumption in OpenFlow networks with in-band control traffic. Our approach also considers performance constraints that are crucial in the correct operation of SDN, such as bounded delay for the control plane traffic and load balance between controllers. First, we present a complete formulation of the optimization problem involving the routing requirements for control and data plane communications. To reduce the time complexity of our model in large-scale topologies we derive a heuristic algorithm. Significant values of energy saving (up to 60%) are reached in the simulations using real topologies and demands data.

The growing energy consumption of communication networks has attracted the attention of the networking researchers in the last decade. In this context, SDN allows a flexible programmability, suitable for the power-consumption optimization problem. In this paper we present an energy-aware routing approach which minimizes the number of links used to satisfy a given traffic demand. Different from previous works, we optimize energy consumption in OpenFlow networks with in-band control traffic. To this end, we start formulating an optimization model that considers routing requirements for control and data plane communications. To reduce the complexity of our model in large-scale topologies, a heuristic algorithm is developed as well. Although it is not widely researched, except for quantitative and heuristic results, we also derive a simple and efficient algorithm for the best controller placement in terms of energy saving. Simulation results confirm that the proposed solution enables the achievement of significant energy savings.

The controller placement problem remains a key aspect of Software Defined Networking (SDN). The selection of a suboptimal controller may impact severely the performance of the control layer and, in consequence, cause a substantial degradation in the data layer. Different approaches for controller placement have been proposed with various objectives in mind, but most of them do not consider the characteristics of the resulting control layer in terms of robustness. In this paper we propose and formalize a complete metric for estimating robustness of a SDN control layer, and we evaluate two heuristics for controller selection and control layer construction: Fast Failover and a simplified version of k-Critical. The results of the performed evaluation indicate that the control layer topology induced by k-Critical is less prone to failures, more robust and more homogenous than those computed by Fast Failover.

The software-defined network (SDN) advocates a centralized network control, where a controller manages a network from a global view of the network. Large SDN networks may consist of multiple controllers or controller domains that distribute the network management between them, where each controller has a logically centralized but physically distributed vision of the network. In this context, a key challenge faced by providers is to define a scalable control network that exploits the benefits of SDN when used in conjunction with efficient management strategies. Most of the control layer models proposed are not concerned with controller scalability, because they assume that commercial controllers are scalable in terms of capacity (quantity of flows processed per second). However, it has been demonstrated that overloads and long propagation delays among controllers and controllers-switches can lead to a long response time of the controllers, affecting their ability to respond to network events in a very short time and reducing the reliability of communication. In this work we define the principles for designing a scalable control layer for SDN, and show the desired control layer characteristics that optimize the management of the network. We address these principles from the perspective of the controller placement problem. For this purpose we improve and evaluate our previous approach, the algorithm called k-Critical. K-Critical discovers the minimum number of controllers and their location to create a robust control topology that deals robustly with failures and balances the load among the selected controllers. The results demonstrate the effectiveness of our solution by comparing it with other controller placement solutions.

This work proposes an algorithm called k-Critical to solve the controller placement problem in Software Defined Networks. K-Critical finds the minimum number of controllers to satisfy a target communication delay between controllers and nodes, Dreq. In addition, the controllers selected create a management architecture that improves the subjacent network performance. In this work we focus on the controllers selection procedure, and show the desired management architecture characteristics that optimize the control and management of the network. The results show that our management trees balance the load among them and reduce the data loss.

Cloud computing builds upon advances on virtualization and distributed computing to support cost-efficient usage of computing resources, emphasizing on resource scalability and on demand services. Moving away from traditional data-center oriented models, distributed clouds extend over a loosely coupled federated substrate, offering enhanced communication and computational services to target end-users with quality of service (QoS) requirements, as dictated by the future Internet vision. Toward facilitating the efficient realization of such networked computing environments, computing and networking resources need to be jointly treated and optimized. This requires delivery of user-driven sets of virtual resources, dynamically allocated to actual substrate resources within networked clouds, creating the need to revisit resource mapping algorithms and tailor them to a composite virtual resource mapping problem. In this paper, toward providing a unified resource allocation framework for networked clouds, we first formulate the optimal networked cloud mapping problem as a mixed integer programming (MIP) problem, indicating objectives related to cost efficiency of the resource mapping procedure, while abiding by user requests for QoS-aware virtual resources. We subsequently propose a method for the efficient mapping of resource requests onto a shared substrate interconnecting various islands of computing resources, and adopt a heuristic methodology to address the problem. The efficiency of the proposed approach is illustrated in a simulation/emulation environment, that allows for a flexible, structured, and comparative performance evaluation. We conclude by outlining a proof-of-concept realization of our proposed schema, mounted over the European future Internet test-bed FEDERICA, a resource virtualization platform augmented with network and computing facilities.

Immediate reservation (IR) and advance reservation (AR) are the two main reservation mechanisms currently implemented on large-scale scientific optical networks. They can be used to satisfy both provisioning delay and low blocking for delay-tolerant applications. Therefore, it seems reasonable that future optical network provisioning systems will provide both mechanisms in hybrid IR/AR scenarios. Nonetheless, such scenarios can increase the blocking of IR if no quality-of-service (QoS) policies are implemented. A solution could be to quantify such blocking performance based on the current network load and implement mechanisms that would act accordingly. However, current blocking analytical models are not able to deal with both IR and AR. In this paper, we propose an analytical model to compute the network-wide blocking performance of different IR/AR classes within the scope of a multiservice framework for optical wavelength-division multiplexing (WDM) networks. Specifically, we calculate the blocking on two common optical network scenarios using the fixed-point approximation analysis: on wavelength conversion capable and wavelength-continuity constrained networks. Performance results show that our model provides good accuracy compared to simulation results, even in a scenario with multiple reservation classes defined by different book-ahead times.

Current Internet and large-scale experimentation applications need to satisfy short provisioning delay and low blocking demands. Both can be guaranteed by using immediate reservation (IR) and advance reservation (AR), respectively. However, the scheduling of both reservation types in the same network can especially degrade the performance of IR if no extra policies are applied. In order to enhance such class-based policies, we need to quantify the future performance of the system, thus requiring to model its behavior. In this paper, we propose the use of a two-fold probability transition Markov chain successfully applied in the past in offset-based reservation systems. Results show the good accuracy of the model to simulation results, even in an scenario with multiple traffic classes defined by different book-ahead times. Such a performance validates its applicability to a wide range of immediate and advance reservation systems.

Some current Internet applications (e.g., Grid/Cloud computing storage, video-conference) demand service differentiation, not only in terms of packet forwarding, but also at the connection level. To satisfy the demands for delay-sensitive and low-blocking applications, immediate reservation (IR) and advance reservation (AR) can be performed. Independent resource reservation of each type of network resources is well-investigated. Nevertheless, it is very likely that both types of requests will need to share network resources. There is also significant demand for a service framework that is able to provide application-aware quality of service (QoS). By using existing scheduling algorithms for IR/AR, we develop a service framework to guarantee relative QoS among different application requests, with and without tolerance to delay and service blocking. Simulation results demonstrate the feasibility of our approach and provide a basis for future development of enhanced IR/AR QoS policies within the control plane of future high-speed networks.

Future optical communication networks are expected to change radically during the next decade. To meet the demanded bandwidth requirements, more dynamism, scalability and automatism will need to be provided. This will also require addressing issues such as the design of highly distributed control plane systems and their associated algorithms to respond to network changes very rapidly. In this work, we propose the use of an ant colony optimization (ACO) algorithm to solve the intrinsic problem of the routing and wavelength assignment (RWA) on wavelength continuity constraint optical networks. The main advantage of the protocol is its distributed nature, which provides higher survivability to network failures or traffic congestion. The protocol has been applied to a specific type of future optical network based on the optical switching of bursts. It has been evaluated through extensive simulations with very promising results, particularly on highly congested scenarios where the load balancing capabilities of the protocol become especially efficient. Results on a partially meshed network like NSFNET show that the ant-based protocol outperforms other RWA algorithms under test in terms of blocking probability without worsening other metrics such as mean route length.

The amount of bursty Internet traffic leads to develop new architectures and technologies, such as Optical Burst Switching (OBS), to efficiently satisfy future bandwidth requirements. Burst loss probability is an important quality of service metric for OBS due to its bufferless characteristic, even

Loss-free schemes are defined to ensure successful packet/burst transmissions in optical packet/burst switching networks. To this end, they rely on a collision-free routing and wavelength assignment (CF-RWA) scheme combined with simple contention resolution mechanisms that guarantee the absence of losses in intermediate links. Here, the CF-RWA problem is studied. In particular, by using graph theory, the problem of finding CF-RWA schemes that minimise the number of wavelengths to serve a given traffic matrix is set. The problem is simplified when it is formulated by using pre-defined sets of non-colliding paths. Within this framework, the problem is shown to be equivalent to finding a given vertex-set colouring of the so-called restriction digraph. Here, two heuristic algorithms are proposed to obtain such vertex-set colourings. One of them provides a suitable CF-RWA without having to solve the minimisation problem. By way of example, the proposed method is applied to the NSFNet and the EON network providing quasi-optimal results.

Contention resolution is a major issue in OBS networks. Several proposals that ensure burst transmissions without losses inside the network have been studied in the literature. Two of these proposals are based on combining a collision-free routing and wavelength assignment scheme with simple contention avoidance/resolution mechanisms. The static approach defines variable offsets and ensures contention avoidance by means of a suitable pre-assignment of offset windows to each communication. The dynamic approach guarantees the successful resolution of all contentions by using a single FDL at each intermediate node. Both proposals are based on giving priority to transmissions coming from the upstream. Hence, when an upstream node misbehaves or changes its transmission traffic pattern, it might delay new burst allocations on downstream nodes, leading to burst losses in the worst case. In this paper we deal with the fairness issue of these proposals. Thus, we introduce simple mechanisms that guarantee the transmission of the committed load for each communication, allowing, at the same time, the dynamic sharing of the spare bandwidth on each wavelength. The proposed mechanisms are analyzed by means of simulation.

This work introduces a new approach to get loss-free burst/packet transmission in optical burst and packet switched networks. To this end, our proposal defines (1) a routing and wavelength assignment (RWA) scheme based on the concept of wavelength tree, and (2) a simple contention resolution mechanism that solves contention using a limited number of fiber delay lines. We show that using the proposed scheme, for a 2-link-connected network with n nodes, there exists communication between any pair of nodes with [n/2] wavelengths. We apply this approach to address the problem of finding (conflict-free) transmission schemes in iterated line digraphs. Such digraphs have proved to be very useful models for dense, easily mutable, and fault tolerant communication networks. Examples of such networks are the well-known De Bruijn and Kautz digraphs and the wrapped butterfly networks. Our study leads us to define a useful tool for obtaining wavelength trees. By way of example, we illustrate the scheme operation for the 2-regular Kautz digraph. Simulation results show a good behavior in terms of transmission delay and resources utilization.

This paper proposes a simple procedure to ensure burst transmission without losses in Optical Burst Switching ring networks. The contention problem is solved by providing a scheme that pre-assigns, to each communication, a given wavelength and an offset window. This approach does not need any additional control information and, since burst contention is avoided, the network capacity and the wavelength utilization are maximized under dynamic traffic assumptions. The study is done by using techniques from graph theory. In particular, the so-called (acyclic) restriction digraphs provide a sharp lower bound for the number of required wavelengths, and support a greedy algorithm for assigning a suitable offset window to each communication. An alternative formulation of the obtained schemes, in terms of matrices, is also discussed. Simulation results of different feasible solutions are provided, showing low transmission delays and quite balanced wavelength utilization.