We propose a novel two-level hierarchical auction model for 5G RAN network slicing that enables financially-aware resource allocation among Mobile Network Operators (MNOs), Mobile Virtual Network Operators (MVNOs), and end users. Compared to prior models that primarily emphasize technical resource utilization, our approach integrates a realistic financial cost framework, including electricity consumption and resale bidding fees into the optimization process. Each level solves a Winner Determination Problem (WDP) using Integer Linear Programming (ILP) and scalable heuristic algorithms. Vickrey–Clarke–Groves (VCG)-based pricing is adopted to ensure incentive compatibility and procedural fairness among bidders. The optimization objective is to maximize the social welfare, defined in this work as the total valuation of accepted requests minus the electricity cost incurred by CU–DU placement. Power consumption is therefore internalized as a cost component in the objective function rather than treated as an independent optimization target. Through extensive simulations over different network topologies and dynamic traffic conditions, we show that the proposed approach achieves higher social welfare compared to baseline methods, while maintaining scalability and satisfying the latency and capacity constraints of heterogeneous network slices.

Reinforcement Learning (RL) has emerged as a powerful tool for automating complex network management tasks, yet its lack of transparency and black-box nature hinder trust and adoption in operational environments. In this work, we focus on explaining the behavior of an $\mathbf{R L}$ agent applied to the problem of network slice admission control. We present a framework that integrates three key components: a Deep Reinforcement Learning (DRL) agent for admission control, an Integer Linear Programming (ILP) model for network slice embedding, and an explanation module for interpreting the DRL agent’s policies, namely Shapley Value Explainable Reinforcement Learning (SVERL). Our analysis aims gives particular attention to cases where the RL agent rejects admitting a network slice request despite sufficient network capacity to provision it, and investigates whether explanations can be used to verify and validate the agent’s behavior prior to deployment approval. Experimental results reveal that the agent’s decisions are primarily influenced by substrate network conditions such as congestion, rather than by the intrinsic characteristics of slice requests. While this conservative policy prevents overload, it also leads to overly cautious rejections. Importantly, the proposed explanation framework provides operators with actionable insights to scrutinize, validate, and refine RL-driven policies before operational deployment.

Customer Premises Equipment (CPE) plays a fundamental role in enabling Software-Defined Wide Area Networks (SD-WANs) to support emerging applications such as Metaverse environments. These environments demand agile, secure, and high-performance network infrastructure to ensure seamless connectivity, low latency, and dynamic resource allocation. As the Metaverse continues to evolve, the need for flexible, efficient, and open CPE solutions becomes increasingly important. This paper presents an open-source SD-WAN CPE software designed to enhance network agility, scalability, and performance, specifically addressing the stringent requirements of next-generation Internet applications. The proposed solution supports deployment on both virtual machines and bare-metal systems, enabling seamless integration into diverse infrastructures. To achieve high-speed packet processing, it leverages the Data Plane Development Kit (DPDK), ensuring low-latency and high-throughput communication. Furthermore, it incorporates different overlay tunneling mechanisms, supporting both site-to-site and hub-and-spoke topologies via WireGuard tunneling technology for secure and efficient connectivity. A key feature of this implementation is its rapid failover capability, powered by Bidirectional Forwarding Detection (BFD), alongside real-time performance monitoring, enabling proactive network optimization and fault tolerance. By combining these advanced capabilities with an open-source framework, this SD-WAN CPE software provides a robust and cost-effective solution for next-generation network deployments.

The growing reliance on digital connectivity has made Internet Service Provider (ISP) networks a critical component of modern society, yet they remain a prime target for cyber threats. In recent years, cyberattacks against ISPs have increased in scale and sophistication, posing severe risks to national security, economic stability, and user privacy. The advent of in-network computing and programmable data plane presents a paradigm shift in network security, offering the flexibility to define, modify, and optimize packet processing logic dynamically. Among these advancements, the P4 programming language plays a crucial role, allowing network operators to implement fine-grained traffic monitoring directly within network devices. By leveraging in-network computation, P4 facilitates real-time anomaly detection, making it a powerful tool for mitigating Distributed Denial of Service (DDoS) attacks. However, orchestrating security functions across a distributed network of P4 switches remains a challenge, requiring an efficient and scalable deployment framework.In this paper, we present an open-source orchestration platform for managing and deploying P4-based security programs to enable real-time DDoS detection. Our solution leverages dynamic programmability to enhance network security. By integrating a novel queue monitoring mechanism directly into the data plane, our approach enables the collection of fine-grained network performance metrics in real-time, facilitating faster and more precise attack detection and mitigation. The proposed framework is highly scalable and adaptable, strengthening ISP networks against evolving cyber threats.

Network slicing is a key 5G technology that enables multiple virtual networks to share physical infrastructure, optimizing flexibility and resource allocation. This involves Mobile Network Operators (MNO), Mobile Virtual Network Operators (MVNOs), and end users, where MNO leases network slices to MVNOs, and then provides customized services. This work considers end-to-end network slicing with a focus on fair sharing and financial-related power efficiency, modeled as a twolevel hierarchical combinatorial auction. At the upper level, an MNO auctions slices to competing MVNOs, while at the lower level, MVNOs allocate resources to end users through their own auctions. Dynamic user requests add complexity to the process. Our model optimizes resource allocation and revenue generation using a pair-bid mechanism and Vickrey-Clarke- Groves (VCG) pricing. The pair-bid approach enhances competition and efficiency, while VCG ensures truthful bidding based on marginal system impact. Simulations validate the model’s effectiveness in resource distribution and financial performance, showing a $\mathbf{1 2. 5} \boldsymbol{\%}$ revenue improvement over the baseline.

Integrating advanced Machine Learning (ML) techniques, such as Federated Learning (FL), with emerging paradigms like Multi-access Edge Computing (MEC) and Software-Defined Wide Area Network (SD-WAN) has gained significant attention. In this demo, we present a simulator designed to optimize resource allocation for FL in MEC SD-WAN environments, taking into account network capacity constraints. We demonstrate in real-time how resource allocation is implemented for FL algorithms and visually analyze the impact of different constraints on performance. Additionally, we introduce a heuristic algorithm for Distributed Federated Learning (DFL) that selects the optimal global aggregator based on resource utilization. We evaluate the proposed heuristic within our simulation framework and compare its performance against Centralized Federated Learning (CFL) and traditional ML techniques. Our results demonstrate how the proposed simulator effectively integrates network and resource allocation, providing valuable insights into the interplay between FL and MEC SD-WAN infrastructure.

Modern enterprises depend on uninterrupted WideArea Network (WAN) connectivity between geographically dispersed sites and cloud services. Software-Defined Wide Area Networks (SD-WANs) have emerged as a compelling solution, offering greater flexibility and cost-efficiency compared to conventional WAN technologies. Ensuring robust availability-defined as the network's readiness to provide connectivity-is crucial for a successful SD-WAN implementation. This work presents a comprehensive, model-based evaluation framework designed to quantify service-oriented availability between pairs of SDWAN endpoints. Our hierarchical modeling approach effectively addresses the complexity and accurately reflects the dependencies among various SD-WAN components. Furthermore, we investigate the impact of a centralized control plane and its associated non-idealities on availability, providing insights into communication delays, sampling frequency of network characteristics, and algorithm execution times through simulation. In-depth experiments demonstrate the evaluation capabilities of our framework, including sensitivity analysis to identify critical components, the effects of various non-idealities, and how different control plane policies can mitigate them. These results offer valuable guidance for optimizing SD-WAN design before real-world implementation.

Software-Defined Wide Area Networks (SD-WANs) provide flexibility and cost-efficiency but still face significant challenges in guaranteeing high availability and consistent performance, particularly when utilizing diverse internet access networks, especially if best-effort like broadband connectivity. Furthermore, reliance on centralized controllers can introduce bottlenecks and single points of failure. This paper outlines a PhD research roadmap aimed at developing a resilient, performant, and extensible SD-WAN framework. The proposed research starts with robust modeling of service-oriented availability considering control plane impacts and quality of service requirements. This will guide us through an availability-aware SD-WAN modular architecture with the function of simplifying the development and test of new features such as: strategies to enhance edge autonomy by reducing real-time controller dependency; integration of predictive awareness tailored to specific internet access networks as LEO satellite based; and experimenting advanced monitoring for intelligent and reactive edgebased traffic engineering. We present the research context, state-of-the-art limitations, core research questions, the methodology combining modeling, simulation, and prototype development, and the expected contributions towards next-generation SD-WAN architectures suitable for research and demanding enterprise applications.

Software-Defined Wide Area Networks (SD-WANs) have emerged as a transformative solution for modern enterprise networking, enabling dynamic traffic management, cost-efficient connectivity, and improved network performance. However, ensuring real-time visibility into network conditions remains a key challenge, as SD-WAN overlay tunnels operate over diverse and often unpredictable underlay networks. Traditional network monitoring techniques, such as active and passive monitoring, face limitations in balancing accuracy, responsiveness, and overhead. To address this challenge, we propose an In-Band Network Telemetry (INT) framework for SD-WANs, leveraging extended Berkeley Packet Filter (eBPF) technology for efficient and flexible packet processing. Our approach enables real-time telemetry data collection at the Customer Premises Equipment (CPE) level, allowing for precise performance monitoring while minimizing additional network overhead. The framework integrates seamlessly with various VPN-based SD-WAN tunnels, including Generic Routing Encapsulation (GRE), IP Security (IPSec), and IPSec over GRE, ensuring adaptability across different deployment scenarios. By embedding telemetry metadata directly into overlay packets, the proposed solution provides continuous monitoring of critical Quality of Service (QoS) metrics, such as One-Way Delay (OWD), Two-Way Delay (TWD), and packet loss rate. Through extensive experimentation, we demonstrate the effectiveness of our INT-enabled SD-WAN framework in accurately detecting network anomalies and ensuring Service-Level Agreement (SLA) compliance. The results validate our approach as a scalable and lightweight monitoring solution for enhancing network observability in SD-WAN deployments.

The rapid increase in connected devices has significantly intensified the computational and communication demands on modern telecommunication networks. To address these challenges, integrating advanced Machine Learning (ML) techniques like Federated Learning (FL) with emerging paradigms such as Multi-access Edge Computing (MEC) and Software-Defined Wide Area Networks (SD-WANs) is crucial. This paper introduces online resource management strategies specifically designed for FL model aggregation, utilizing intermediate aggregation at edge nodes. Our analysis highlights the benefits of incorporating edge aggregators to reduce network link congestion and maximize the potential of edge computing nodes. However, the risk of network congestion persists. To mitigate this, we propose a novel aggregation approach that deploys an aggregator overlay network. We present an Integer Linear Programming (ILP) model and a heuristic algorithm to optimize the routing within this overlay network. Our solution demonstrates improved adaptability to network resource utilization, significantly reducing FL training round failure rates by up to 15% while also alleviating cloud link congestion.

Wide Area Networks (WANs) are essential for enabling communication among different branches within enterprises, ultimately improving operational efficiency and fostering economic advancement. However, the traditional WAN solutions often struggles to meet the diverse needs of geographically dispersed organizations. The advent of Software-Defined Wide Area Network (SD-WAN) solutions represents a paradigm shift, harnessing Software-Defined Networking (SDN) principles to transcend the limitations inherent in traditional WAN architectures. However, the necessity for advanced monitoring solutions persists as fundamental in guaranteeing optimal performance and unwavering reliability. This paper proposes the integration of In-band Network Telemetry in SD-WANs through Extended Berkeley Packet Filter (eBPF) technology to assess network performance parameters, such as the delay, in real-time. This work addresses the increasing demand for performance, security and reliability in such network architectures by leveraging INT capabilities for monitoring and manipulating data packets. Through the exploitation of eBPF’s features and network programmability, we introduce a novel approach aimed at measuring the end-to-end delay across SD-WAN deployments.

Wild animals pose significant challenges in agricultural environments, necessitating effective monitoring solutions for both agricultural management and wildlife preservation. Leveraging novel deep learning algorithms for real-time animal detection holds great promise in enhancing wildlife monitoring activities. This work presents a performance evaluation of two deep-learning based image recognition models (YOLOv8 and YOLOv9) with application of color space augmentation to simulate different lightning scenarios in real-life environments. Our results show that the detection accuracy is increased by the augmented color spaces versus the natural ones, as the proposed technique is able to strengthen the model’s robustness against environmental changes. Furthermore, we consider the deployment of these models on far edge devices, such as trap cameras with GPUs, where real-time analysis of wildlife activity is crucial for both management and conservation efforts.

In the context of the ever-evolving 5G landscape, where network management and control are paramount, a new Radio Access Network (RAN) as emerged. This innovative RAN offers a revolutionary approach by enabling the flexible distribution of baseband functions across various nodes, all tailored to meet the ever-shifting demands of both system requirements and user traffic patterns. As users move within the network, the need to anticipate and strategically position these baseband functions becomes crucial for seamless network operation. Traditionally, this challenge has been tackled through a two-step process: first, forecasting traffic patterns, and then optimizing resource allocation accordingly. However, this approach falls short in guaranteeing an efficient placement when actual traffic demands surge onto the network. It often leads to resource overbooking, constraint violations, and excessive power consumption, putting strain on the network’s capabilities. In this paper, we introduce a novel framework based on a black-box optimization approach. This tool empowers prediction algorithms not just with historical traffic data but also with insights from optimization outcomes. The goal is to minimize a loss function related to power consumption and constraint violation: this ensures a predicted placement that is feasible and whose power is close to optimal. This approach ensures that the predicted placement is both feasible and power-efficient, bridging the gap between theoretical prediction and practical implementation. Remarkably, our proposed method, while potentially sacrificing some degree of traffic prediction accuracy, outperforms the conventional two-step approach by delivering a more efficient baseband function placement.

The continuous growth of advanced services integrating processing, storage, real-time data exchange, and transmission capacities highlights the importance of Quality of Service (QoS) and Quality of Experience (QoE). In particular, it is very relevant for enterprises that rely on Wide Area Networks (WANs) for reliable and efficient communication between their headquarters and branches. With the evolution of new applications with stringent requirements, ensuring high-performing WANs is a critical priority for businesses looking to remain competitive and provide a seamless customer experience. This paper explores using Rein-forcement Learning (RL) algorithms on Software-Defined Wide Area Network (SD-WAN) to improve QoS and reduce costs. SD-WAN allows for the dynamic reconfiguration of network devices in real-time, better meeting network measurements and service requirements. By leveraging self-learning techniques such as RL, which exploits feedback mechanisms, we can improve network availability by automatically routing traffic over existing network technologies. We also compare two SD-WAN topology scenarios, including direct WAN connections between Customer Premises Equipment (CPEs) within enterprise premises and CPEs used as peering points for traffic routing. Our approach shows promising results regarding network performance and cost-effectiveness, which can benefit businesses looking to improve their network infrastructure.

Quality of Service (QoS) and Quality of Experience (QoE) are the most relevant requirements for new advanced services comprising integrated cooperation between processing, storage, sensing, and transmission capabilities. As reliance on technology grows, businesses increasingly turn to Wide Area Networks (WANs) connectivity solutions to ensure reliable and efficient communication between their locations and cloud data centers. With new applications with stringent requirements, QoS and QoE have become critical priorities for companies looking to remain competitive and provide a seamless customer experience. As a result, many organizations require reliable and high-performing WANs to effectively transmit critical data between their branches and cloud data centers. We examine Multi-Protocol Label Switching (MPLS technology), which is the historical choice for WANs but has significant disadvantages in cost and performance. Then, we analyze the novel Software-Defined Wide Area Network (SD-WAN) technology that allows for real-time flexible, dynamic reconfiguration of network devices to meet network measurements and service requirements. Furthermore, we introduce a first work of my Ph. D. that examines the use of a Reinforcement Learning algorithm in a new SD-WAN topology scenario in which we consider both direct WAN connections between Customer Premises Equipment (CPEs) located within enterprise premises and CPEs used as peering points for traffic routing. This paper presents our initial analysis of these technologies and the main ideas guiding me in my Ph. D. program.

Network slicing is an important characteristic of 5G/6G networks that increases flexibility and enables different applications over a single infrastructure. The physical resources are partitioned to create virtualized networks, each dedicated to services with specific requirements. Several entities participate in network slicing, including Mobile Network Operators (MNOs), Mobile Virtual Network Operators (MVNOs), and users. An MNO owns the physical network infrastructure and the resources. MVNOs lease resources from the MNO and operate as service providers towards their subscribers. The goal of this work is to optimize the end-to-end network slicing process to provide services to users with a fair sharing of resources. We model this problem as a hierarchical combinatorial auction with a modified Vickrey-Clarke-Groves pricing mechanism. In the upper-level auction, an MNO is the seller supplying Network Slice to several MVNOs, who act as the bidders. In the lower-level auction, each MVNO holds an auction as a seller delivering services to their subscribed end-users, who play the role of bidders. We formulate and solve the Winner Determination Problem using mathematical programming and heuristic algorithms. The simulations show that the model can achieve fair sharing of resources, and it enables improving the MNO and MVNO revenue.

The increased demand for high-quality Internet connectivity resulting from the growing number of connected devices and advanced services has put significant strain on telecommunication networks. In response, cutting-edge technologies such as Network Function Virtualization (NFV) and Software Defined Networking (SDN) have been introduced to transform network infrastructure. These innovative solutions offer dynamic, efficient, and easily manageable networks that surpass traditional approaches. To fully realize the benefits of NFV and maintain the performance level of specialized equipment, it is critical to assess the behavior of Virtual Network Functions (VNFs) and the impact of virtualization overhead. This paper delves into understanding how various factors such as resource allocation, consumption, and traffic load impact the performance of VNFs. We aim to provide a detailed analysis of these factors and develop analytical functions to accurately describe their impact. By testing VNFs on different testbeds, we identify the key parameters and trends, and develop models to generalize VNF behavior. Our results highlight the negative impact of resource saturation on performance and identify the CPU as the main bottleneck. We also propose a VNF profiling procedure as a solution to model the observed trends and test more complex VNFs deployment scenarios to evaluate the impact of interconnection, co-location, and NFV infrastructure on performance.

Today, more and more enterprises are embarking on a digital transformation where most of their applications are hosted in the Cloud. As a result, a reliable Wide Area Network (WAN) has become a primary need to interconnect their distributed branch offices and data centers that accommodate those applications. Software-Defined Wide Area Network (SD-WAN) represents the most promising technology solution for next-generation enterprise networks, being able to increase network agility and reduce costs. In this paper, we present an experimental SD-WAN solution capable of running and optimizing delay-sensitive high-priority services, such as real-time video streaming, while minimizing downtime caused by network failures. This solution comprises a monitoring and a traffic engineering system for SD-WAN. The first consists of a Transport-layer Passive Monitoring (TPM) system based on extended Berkeley Packet Filter (eBPF) technology with the goal of monitoring TCP flows; the second consists of an application, running inside the SD-WAN controller, with the goal of orchestrating the network traffic in consideration of the monitoring measurements by ensuring rapid recovery and resilience in case of unexpected congestion events. We validate our solution over two SD-WAN testbeds: the first is hosted in our laboratory at Politecnico di Milano, while the second is deployed in a municipal network of an Italian city. Results show that our SD-WAN solution can increase the overall service availability while meeting the stringent QoS requirements of delay-sensitive services.s

The 5G Radio Access Network (RAN) virtualization aims to improve network quality and lower the operator’s costs. One of its main features is the functional split, i.e., dividing the instantiation of RAN baseband functions into different units over metro-network nodes. However, its optimal placement is non-trivial: it depends on the application requirements and on the expected traffic volume, whose daily variation highly impacts the total power consumption. Current optimization solutions fail to provide a placement solution capable of handling traffic fluctuations. In fact, the standard machine learning algorithms used in the literature for planning the network resources in advance result in an allocation that is inadequate to carry the actual traffic at all the time-slots. Hence, we must reserve an artificial buffer capacity in the nodes to ensure feasibility. Instead, our proposed method exploits a fine-grained two-step multi-task algorithm that predicts the mean and quantile traffic, making the artificial capacity no longer necessary. The subsequent placement uses mixed-integer linear programming and a heuristic. The former considers the expected traffic in the objective function (to estimate costs) and the quantile in the constraints (to enforce capacity limits). The heuristic combines the mean and quantile results to minimize the power and comply with the requirements. While using sufficiently large artificial buffers guarantees robustness with a mild power increase compared to the oracle, the fine-grained multi-task model improves the results, reducing the power consumption compared to the mean and meets all constraints. The heuristic enables significant computational time reduction.

The demand for reliable and efficient Wide Area Networks (WANs) from business customers is continuously increasing. Companies and enterprises use WANs to exchange critical data between headquarters, far-off business branches and cloud data centers. Many WANs solutions have been proposed over the years, such as: leased lines, Frame Relay, Multi-Protocol Label Switching (MPLS), Virtual Private Networks (VPN). Each solution positions differently in the trade-off between reliability, Quality of Service (QoS) and cost. Today, the emerging technology for WAN is Software-Defined Wide Area Networking (SD-WAN) that introduces the Software-Defined Networking (SDN) paradigm into the enterprise-network market. SD-WAN can support differentiated services over public WAN by dynamically reconfiguring in real-time network devices at the edge of the network according to network measurements and service requirements. On the one hand, SD-WAN reduces the high costs of guaranteed QoS WAN solutions (as MPLS), without giving away reliability in practical scenarios. On the other, it brings numerous technical challenges, such as the implementation of Traffic Engineering (TE) methods. TE is critically important for enterprises not only to efficiently orchestrate network traffic among the edge devices, but also to keep their services always available. In this work, we develop different kind of TE algorithms with the aim of improving the performance of an SD-WAN based network in terms of service availability. We first evaluate the performance of baseline TE algorithms. Then, we implement different deep Reinforcement Learning (deep-RL) algorithms to overcome the limitations of the baseline approaches. Specifically, we implement three kinds of deep-RL algorithms, which are: policy gradient, TD- λ and deep Q-learning. Results show that a deep-RL algorithm with a well-designed reward function is capable of increasing the overall network availability and guaranteeing network protection and restoration in SD-WAN.

Optical shuffle-exchange networks (SENs) have wide application in different kinds of interconnection networks. This article proposes an approach to construct modular optical SENs, using a set of arrayed waveguide gratings (AWGs) and tunable wavelength converters (TWCs). According to the wavelength routing property of AWGs, we demonstrate for the first time that an AWG is functionally equivalent to a classical shuffle network by nature. Based on this result, we devise a systematic method to design a large-scale wavelength-division-multiplexing (WDM) shuffle network using a set of small-size AWGs associated with the same wavelength set. Combining the AWG-based WDM shuffle networks and the TWCs with small conversion range, we finally obtain an AWG-based WDM SEN, which not only is scalable in several ways, but also can achieve 100% utilization when the input wavelength channels are all busy. We also study the routing and wavelength assignment (RWA) problem of the AWG-based WDM SEN, and prove that the self-routing property and the nonblocking routing conditions of classical SENs are preserved in such AWG-based WDM SEN.

Recently, machine learning (ML) has attracted the attention of both researchers and practitioners to address several issues in the optical networking field. This trend has been mainly driven by the huge amount of available data (i.e., signal quality indicators, network alarms, etc.) and to the large number of optimization parameters which feature current optical networks (such as, modulation format, lightpath routes, transport wavelength, etc.). In this paper, we leverage the techniques from the ML discipline to efficiently accomplish the routing and wavelength assignment (RWA) for an input traffic matrix in an optical WDM network. Numerical results show that near-optimal RWA can be obtained with our approach, while reducing computational time up to 93% in comparison to a traditional optimization approach based on integer linear programming. Moreover, to further demonstrate the effectiveness of our approach, we deployed the ML classifier into an ONOS-based software defined optical network laboratory testbed, where we evaluate the performance of the overall RWA process in terms of computational time.

In distributed SDN architectures, the network is controlled by a cluster of multiple controllers. This distributed approach permits to meet the scalability and reliability requirements of large operational networks. Despite that, a logical centralized view of the network state should be guaranteed, enabling the simple development of network applications. Achieving a consistent network state requires a consensus protocol, which generates control traffic among the controllers whose timely delivery is crucial for network performance. We focus on the state-of-art ONOS controller, designed to scale to large networks, based on a cluster of self-coordinating controllers. In particular, we study the inter-controller control traffic due to the adopted consistency protocols. Based on real traffic measurements and the analysis of the adopted consistency protocols, we develop some empirical models to quantify the traffic exchanged among the controllers, depending on the considered shared data structures, the current network state (e.g., topology) and the occurring network events (e.g., flow or host addition). Our models provide a formal tool to be integrated into the design and dimension the control network interconnecting the controllers. Our results are of paramount importance for the proper design of large SDN networks, in which the control plane is implemented in-band and cannot exploit dedicated network resources.

The high predictability in human mobility patterns is a valuable information to optimize resource allocation and increase energy efficiency by effectively adapting to expected traffic load variations. In this work, we addressed the impact of human mobility patterns of mobile users in a metro area over the aggregated traffic offered to the mobile metro-core networks. Exploiting predictable trends recognized in aggregated traffic, we propose two optimization methods to reduce the energy consumption in the optical layer of metro-core networks. Our results display energy savings of more than 20% by introducing load adaptive network operation.

In distributed SDN architectures, the network is controlled by a cluster of multiple controllers. This distributed approach permits to meet the scalability and reliability requirements of large operational networks. Despite that, a logical centralized view of the network state should be guaranteed, enabling the simple development of network applications. Achieving a consistent network state requires a consensus protocol, which generates control traffic among the controllers whose timely delivery is crucial for network performance. We focus on the state-of-art ONOS controller, designed to scale to large networks, based on a cluster of self-coordinating controllers, and concentrate on the inter-controller control traffic. Based on real traffic measurements, we develop a model to quantify the traffic exchanged among the controllers, which depends on the topology of the controlled network. This model is useful to design and dimension the control network interconnecting the controllers.

Public transport plays an importation role in our daily life. The information related to passengers satisfaction is very beneficial for optimizing the transportation service. This paper investigates an application of mobile crowd sensing to detect and analyze the riding quality of public transport vehicles. The lightweight system leverages sensors equipped on participants' smartphones to collect surrounding information. By analyzing the uploaded data at a server, we are able to estimate both aggressive driving behaviors and environment contexts. Series of data processing methods are exploited to overcome the affection of body movement and road condition, and crowd sourcing is applied to improve the robustness of the results. We have tested this system in 3 different transportation in 3 cities. The results indicate that the system can provide sufficient accuracy (up to 91% with 7 phones) to identify dozens of riding-comfort metrics.

This paper defines a procedure for designing the architecture of multistage switching networks. Our proposal is the result of a systematic study of the properties of multistage networks on one side and of optical switching systems on the other side. Modularity of the implementation is the primary concern, allowing the construction of a generic-size fabric by the simple cascading of multiple-stage modules. In this paper, we show in detail the application of the approach to a family of banyan networks, although the method can be extended to other types of networks. The designed architecture can be exploited for various implementation technologies, for instance, integrated optics and free-space optics. The method can even be applied to electronic VLSI/ULSI circuits. After presenting the design procedure, we discuss practical implementations in different technology scenarios. The main contribution of this paper is to simplify the building of large switches by cascading a number of switching modules that combine the internal stage architecture with the interconnection between stages.

Interconnection systems inside switching equipment, high-performance computers and data-centers are nowadays facing more and more demanding requirements. Optical interconnections based on multistage switching networks provide more bandwidth and less energy consumption compared to electronic counterparts. In this work a procedure to design the architecture of optical multistage switching networks is proposed which exploits the properties of multistage networks, on one side, and of optical switching systems on the other side. Thanks to the modularity of the architecture, a generic-size fabric can be implemented by simply cascading multiple stage-modules. In this paper we show in details the application of the approach to the Extended Generalized Shuffle (EGS) networks, though the method can be extended to other types of networks. The proposed procedure supports various implementation technologies, as, for example, integrated optics with micro-ring resonators, free- space optics with 2-D MEMS, networks on chip.

Future switching and interconnection fabrics inside switching equipment, high-performance computers and datacenters will require more throughput and more energy efficiency. Optical technology provides many opportunities of improvement of both features compared to electronic counterparts. This work defines a procedure to design the architecture of optical multistage switching networks. Modularity of the implementation is the primary concern, allowing for the construction of a genericsize fabric by the simple cascading of multiple stage-modules. In this paper we show in details the application of the approach to a family of banyan networks. The designed architecture can be exploited for various implementation technologies, as, for instance, integrated optics with micro-ring resonators, free-space optics with 2-D MEMS, networks on chip.

Future interconnection subsystems for switches and routers must overcome physical limitations of current electronic backplanes in order to achieve aggregate bandwidth much greater than today. Driven by this consideration we study the scalability of switching architectures implementing backplanes based on optical interconnection technology. In particular, this paper deals with the issue of interconnecting the line cards of a switch/router exploiting the wavelength agility of the transmitters and the switching/filtering capabilities of Micro Ring Resonators. To accomplish this task, the resonators deployed in the backplane are both tunable and fixed, thus defining a hybrid architecture. In our analysis we address feasibility and scalability issues in terms of maximum number of interconnected cards, taking various design parameters into account. Moreover, we perform a preliminary power consumption analysis of the proposed backplane architecture.

We present a novel architecture for strictly non-blocking multistage photonic switches implemented using tunable wavelength converters and arrayed waveguide gratings. Clos networks, studied mostly in electronic domain, are required when the physical switching needs exceed the capacity of the largest feasible single crossbar switch. Unfortunately, straightforward extensions of these networks to the photonic domain show that the switch size has to be severely limited by the coherent crosstalk in each of the AWGs. We describe how recursive Clos networks free of coherent crosstalk can be built taking into account the current physical limitations of arrayed waveguide gratings.

This paper studies the optical shuffle-exchange interconnection network based on Arrayed Waveguide Gratings (AWGs). It proposes implementations of the network suitable to be exploited in an optical backplane that interconnects line-cards of a high-performance switch or router. A procedure to design AWG interconnection stages with zero coherent crosstalk is presented and applied to the perfect-shuffle pattern, the building-block of the shuffle-exchange. The scalability of the proposed architecture is analyzed with a simple approach.

Abstract—It is generally admitted that interdomain peering links represent nowadays the main bottleneck of the Internet, particularly because of lack of coordination between providers, which use independent and “selfish ” routing policies. We are interested in identifying possible “light ” coordination strategies that would allow carriers to better control their peering links while preserving their independence and respective interests. We propose a robust multipath routing coordination framework for peering carriers, which relies on the multiple-exit discriminator (MED) attribute of Border Gateway Protocol (BGP) as signaling medium. Our scheme relies on a game theory modeling, with a non-cooperative potential game considering both routing and congestions costs. Peering equilibrium multipath (PEMP) coordination policies can be implemented by selecting Pareto-superior Nash equilibria at each carrier. We compare different PEMP policies to BGP Multipath schemes by emulating a realistic peering scenario. Our results show that the routing cost can be decreased by roughly 10 % with PEMP. We also show that the stability of routes can be significantly improved and that congestion can be practically avoided on the peering links. Finally, we discuss practical implementation aspects and extend the model to multiple players highlighting the possible incentives for the resulting extended peering framework. Index Terms—Border Gateway Protocol (BGP), game theory, interdomain routing, multiple-exit discriminator (MED), multipath, peering. I.

In multi-domain multi-carrier networks the effective use of network resources shall be achieved while guaranteeing an adequate level of confidentiality and scalability. A candidate solution to perform effective multi- domain Traffic Engineering (TE) is based on a combination of (i) hierarchical routing and (ii) path computation procedures. Hierarchical routing identifies the domain sequence to cross while path computation computes the strict end to end path. In this multi-domain study we first propose a hierarchical instance of BGP (HBGP) dedicated to TE information only. Then we propose and evaluate the integration of HBGP with path computation procedures based on IETF PCE architecture. Simulation results show that the hierarchical HBGP-PCE architecture, compared to current routing solutions based on BGP only, significantly improves the overall network resource utilization. In addition, this study identifies the network scenarios in which the aforementioned HBGP-PCE features provide significant advantages. Finally, the experimental implementation of the proposed HBGP-PCE architecture in a network testbed composed of commercially available routers shows the viability of the solution in real networks.

The new Carrier Ethernet services offered by providers require not only a huge amount of bandwidth, but also a more flexible access to bandwidth that the traditional transport networks, based on circuit-switching, can hardly provide. Carriers have nowadays the option of migrating to new layer-2 frame-switched technologies developed ad hoc to support Carrier Ethernet. An important question is whether this migration is really cost effective. This paper deals with the problem of designing a transport network able to support Carrier Ethernet. We compare two network models, one based on layer-2 switching and Ethernet interfaces, the other based on legacy circuit switching and SDH interfaces. Minimization of investments for network interfaces is carried out for the two models, given the same static-traffic matrix to be supported. Both an ILP formulation and a heuristic method are proposed to solve the design problem. The results presented for comparison are obtained by applying the optimization procedures to a case-study network.

In this paper we provide a mathematical ILP model for the Regeneration Placement Problem (RPP) which minimizes the total number of regeneration nodes allocated in a translucent optical network ensuring that all the node pairs can always reach one another via two link-disjoint lightpaths under physical-impairment constraints. Since RPP is NP-complete, large-site design problem can not be solved relying upon exact approaches. We then propose a game-theoretic approach to model RPP as a non-cooperative game and solve it applying the best response dynamic concept. Finally, we evaluate the performance of the proposed approach in terms of closeness of the obtained results to these provided by ILP: a MILP formulation is given in order to study the quality of the Nash equilibria by comparison to Price-of-Anarchy and Price-of-Stability bounds.

It is generally admitted that Inter-domain peering links represent nowadays the main bottleneck of the Internet, particularly because of lack of coordination between providers, which use independent and "selfish" routing policies. We are interested in identifying possible "light" coordination strategies, that would allow carriers to better control their peering links, while preserving their independence and respective interests. We propose a robust multi-path routing coordination framework for peering carriers, which relies on the MED attribute of BGP as signalling medium. Our scheme relies on a game theoretic modelling, with a non-cooperative potential game considering both routing and congestions costs. Peering Equilibrium MultiPath (PEMP) coordination policies can be implemented by selecting Pareto-superior Nash equilibria at each carrier. We compare different PEMP policies to BGP Multipath schemes by emulating a realistic peering scenario. Our results show that the routing cost can be decreased by roughly 10% with PEMP. We also show that the stability of routes can be significantly improved and that congestion can be practically avoided on the peering links.

A design technique for reliable optical transport networks is presented. The network is first dimensioned in order to carry a given set of static protected optical connections, each one routed maximizing its availability. The network can be further optimized by minimizing the number of fibers to be installed, while keeping a control on connection availability, which can remain the same or decrease by a prefixed margin factor. Design and optimization algorithms are provided for networks adopting dedicated and shared path-protection. The optimization approach is heuristic. Results obtained by applying the proposed technique to two case-study networks are shown and discussed. These two case-study experiments are carried out exploiting a realistic model to evaluate terrestrial and submarine optical link availability.

The paper presents and compares various unicast nonblocking architectures to be used into space-domain photonic switching networks. All the analyzed architectures have been evaluated and compared considering a possible physical implementation based on guided-wave structures realized with integrated optics technology. Some properties including number of switching elements required, blocking performance, number of waveguide crossovers, system attenuation, and signal-to-noise ratio are evaluated and analyzed. The main purpose of this work is to review the state-of-the-art of optical guided-wave space-switching architectures and to provide a relevant set of technical elements useful in the selection of architectures to be used in all-optical cross-connect implementation.

We present a heuristic method to optimize the capacity of a WDM mesh network assigning working and protection capacity to a set of static optical-connection requests under the link-disjoint constraint. The method can be applied to networks supporting either dedicated or shared path-protection. Routing and fiber and wavelength assignment (RFWA) are jointly performed for all the lightpaths with the aim of minimizing the number of fibers in the network. The employment of multiple prioritized heuristic criteria and an efficient link disjoint paths search algorithm are proposed. With less computational effort, the proposed heuristic approach allows one to obtain good suboptimal results compared to the exact integer-linear-programming optimization. After introducing our design approach, we discuss the optimization of particular case-study networks under various conditions and compare the results.

Efficient planning and optimization of wavelength division multiplexing networks is an important issue today. Integer linear programming (ILP) is the most used exact method to perform this task. We propose a new ILP formulation that allows to solve optimization with less computational effort compared to other ILP approaches. This formulation applies to multifiber mesh networks with or without wavelength conversion, when either the total fiber number or the total fiber length is the cost function to be minimized. After presenting the formulation we discuss the results we obtained by exploiting it in the optimization of two case-study networks.

Optical cross-connects (OXCs) are gaining an increasing importance in high-capacity wavelength-division multiplexing networks. In this work we propose a theoretical approach for the analysis and design of these optical-switching systems. Our method is based on a space-switching equivalent representation which allows one to easily classify a general OXC architecture, identify its blocking properties and understand the trade-off between space and wavelength switching-domains. We explain this method and demonstrate its application to some of the best known OXC implementations presented so far in the literature.

The development of optical cross-connect architectures is a very important topic today. We consider here in particular the class of optical space-division switching fabrics configured as multistage structures built with 2/spl times/2 optical switching elements (SEs) and derived from a combination of vertical replication and horizontal expansion of Banyan networks. We determine the necessary and sufficient conditions for these matrices to be rearrangeably nonblocking and free of first-order crosstalk in SEs. This impairment is one of the major limitations in optical cross-connect performance. We focus on rearrangeable matrices since they have lower complexity than their strict-sense nonblocking counterparts. Given the current high cost of optical SEs, the rearrangeable solution looks attractive today.

The future growth in the demand of transmission bandwidth makes the development of all-optical transport networks and optical cross-connects very important. We consider rearrangeable non-blocking switching fabrics configured in a multistage structure with very simple switching elements. One of the problems of these networks is the crosstalk: a wavelength approach is used here showing that two wavelengths suitably assigned are sufficient to solve any blocking and crosstalk problems. We compare this type of networks, called rearrangeable networks with zero non-filterable crosstalk, with other networks, showing that they give the best performance when employed to implement optical-cross-connects.

The advantages of employing passive optical architectures in the access network have been largely recognized. Particularly, developments in optical technologies have made the realization of wavelength division multiplexing passive optical networks (WDM-PONs) feasible and cost-effective. These networks are more future-proof than conventional PONs, thanks to their intrinsic optical transparency and their extremely high transmission capacity. A very useful optical routing device, called a waveguide grating router, is the basic building-block of new PON architectures capable of connecting a large number of users or to improve the use of the optical bandwidth. In this paper the feasibility of these architectures is discussed by considering the costs and the technological limitations on the optical components.

Dynamic wavelength assignment in a WDM all-optical network with sparse wavelength conversion is discussed in this paper. The performance of unidirectional and bidirectional ring networks in terms of blocking probability, conversion gain and fairness are evaluated under various types of offered traffic patterns. Smooth and peaked patterns have been considered in addition to the classical Poisson traffic type. It is shown how the use of wavelength converters affects the blocking performance of connections and, more importantly in terms of network fairness, its dependence on the source-destination distance.

The substantial growth expected in the near future in the demand of transmission bandwidth to be used in a dynamic and flexible transport network makes the development of all-optical digital crossconnect architectures very important. We consider here in particular the class of the rearrangeable non-blocking space-division switching fabrics configured as multistage structures built with very simple optical switching elements. Rearrangeable networks look today more attractive than strict-sense non-blocking networks since the former have a lower complexity and are compatible with the data loss rate required in a circuit-switched network even with current technology of optical switching devices. We face one the most important problems which arises in a multiple space-channel optical system such as a photonic switching fabric, that is the build-up of crosstalk noise on a certain channel due to the interference with other signals inside the system. We find here the design rules to configure a minimum-cost rearrangeable photonic switching network where the most serious cause of crosstalk (interference inside the switching elements) is eliminated.