Traditional rule-based or static management approaches struggle to cope with the dynamic, multi-layered nature of 5G/6G networks, creating a strong motivation for AI-native solutions – management systems built from the ground up with artificial intelligence – to enable autonomous, real-time network control. In this work, we introduce GNN-ATIVE, an AI-native orchestration framework that leverages Graph Neural Networks (GNNs) and knowledge graphs (KGs) in a unified graph-based paradigm for network management. GNN-ATIVE uses a semantic knowledge graph to represent the network’s state and context, employing standard ontologies to ensure consistency and interoperability. Building on this foundation, we design Knowledge Graph enabled Generative Pretrained Transformer (KG-GPT), a novel graph-to-graph Transformer model that performs knowledge-driven reasoning on the KG. KG ingests the structured network state (nodes, links, and attributes) and infers optimal configurations or management actions, serving as a high-level decision engine for the orchestrator. We implement and evaluate GNN-ATIVE on an Optical Transport Network (OTN) testbed using real network components. The results demonstrate that GNN-ATIVE can effectively manage OTN resources and adapt to network changes while achieving low-latency inference for decision making.

IoT use cases in the domain of critical infrastructure or disaster prevention, such as flood monitoring, have become increasingly popular. These are often implemented via low power wide area networks (LPWAN). LPWAN outages can prevent the delivery of data in such settings, highlighting the importance of transmission resiliency in IoT applications. In this work, we analyze how short-term LPWAN failures can be avoided by adding a payload history to the current message or using multiple LPWAN communication channels. Long-term failures are investigated with a Monte-Carlo-based simulation with a generalized linear model to understand the factors that affect the mean time to failure (MTTF) in such networks. If multiple different LPWAN interfaces are available in an IoT device, a simple parallel use of all networks increases reliability but consumes too much energy for battery-powered devices and can violate duty cycles. For this reason, we propose the MultiPath Low Power Wide Area Network (MP-LPWAN) protocol for IoT devices, which combines multiple LPWANs to deal with such failures. MP-LPWAN improves overall IoT system performance by dynamically adjusting transmission parameters and interface priority to adapt to changing network conditions. We implemented MP-LPWAN on a dual-LPWAN testbed and empirically validated its performance.

Network management systems for beyond 5G (B5G) and 6G networks today require efficient approaches for handling increased heterogeneity, network-function dis-aggregation, performance requirements, and optimizing networks to support highly diverse use cases. While the Open-Radio Access Network (O-RAN) Service Management and Orchestration (SMO) frameworks efficiently manage RAN and cloud infrastructure, the fragmentation of management platforms across RAN, Core Network (CN), and Transport Network (TN) introduces operational inefficiencies, particularly in Non-Public Networks (NPNs) deployments. This paper proposes a novel extension to the O-RAN SMO architecture, integrating CN and TN management functionalities into a unified control framework. By exploiting AI/ML-driven$\mathrm{x} / \text{rApps}$and a converged data analytics pipeline, the proposed architecture enhances SMO's fault management, resource optimization, and service continuity capabilities. Our implementation validates the feasibility of the proposed SMO extension, demonstrating subscriber-specific QoS assurance through O-RAN-based mobility management mechanisms. The proposed approach successfully shows how RAN-/Core-converged SMOs enable significant enhancements to O-RAN's x/rApps, allowing for subscriber-specific as well as application-specific differentiated QoS assurance.

This paper presents a framework that integrates Generative AI as a Service (GAIaaS) into Service Management and Orchestration (SMO) systems to enable intent-based automation. By leveraging ChatGPT-4o's advanced natural language capabilities, the system interprets user intents and generates policy-driven service chain configurations. Prompt engineering techniques are employed to evaluate the model's performance across key areas, including response time for intent processing, token usage efficiency, repeatability of outputs, infrastructure cost, and multilingual support. The framework consists of various components such as orchestration engine, cloud network function controller, and SDN controller and automates service chain design and resource management. The obtained results demonstrate its reliable and scalable performance across different scenarios. However, challenges related to handling large prompts and sustaining performance under high loads have been identified. This work highlights the potential of GAIaaS to provide scalable, adaptive, and intelligent network automation.

It is essential to be able to identify hidden anomalies in order to fully automate optical networks. This requires specific features from the application programming interfaces (APIs) used by the control plane and network monitoring solution. One of the solutions, Transport API (TAPI), utilizes advanced techniques in telemetry streaming. The update policy in TAPI enables key performance indicators (KPIs) to be transmitted only when changes are detected. In this paper, we explore how the update policy configuration of TAPI and the use of unsupervised learning (UL) interact in detecting previously unseen anomalies. Results reveal various trade-offs that network operators need to consider, including compute and time overhead, as well as the overall accuracy of UL.

In this work, we propose a network hierarchy to enable low-power IoT services applying the directed-acyclic graph (DAG) based Distributed Ledger Technology (DLT) IOT $\Lambda$ Streams on top of the low-power wide-area network (LPWAN) protocol LoRaWAN. For LPWAN technologies, the challenges for applying distributed ledger application layer protocols are the small payload sizes and duty cycle regulations with open frequency spectrum protocols and the transport protocol limitations in massive-machine-type mobile communication protocol derivations (e.g. NB-IoT). For low-power embedded end devices, additional challenges may arise in terms of local computation limitations, which can be a limiting factor in performing necessary DL protocol-related functions at the device level (e.g. hashing, encryption) to maximize DL-related technology benefits end-to-end. In this paper, we show the concept of a scalable, feeless, low-power communication solution for reliable and secure data transmission and processing in sensor networks employing the DL technology IOT $\Lambda$ based Streams protocol together with the LPWAN protocol LoRaWAN and demonstrate our design for the use case of a smart metering application.

Traditional Machine Learning (ML)-based Intrusion Detection Systems (IDS) are convenient for detecting cyber-attacks like Distributed Denial of Service (DDoS) attacks but have some drawbacks, such as privacy risks and high communication overhead as all traffic data has to be forwarded to a central entity which runs the ML model for classification. Federated Learning (FL), as a decentralized ML approach, provides a promising solution to this issue by allowing clients to train models locally and only exchange model parameters with a central entity, thus enhancing privacy and reducing communication overhead. Despite its benefits, FL-based IDS systems also face challenges such as handling imbalanced and non-IID traffic data and the need for continuous model retraining. This paper introduces an advanced FL-based IDS which integrates several components such as Variational Autoencoders (VAEs), Federated Averaging with Momentum (FedAvgM) model parameter aggregation, client sampling and retraining mechanisms to overcome these challenges. Our evaluation which includes comparisons to non-FL IDS setups, shows significant improvements of our FL-based IDS with regard to detection accuracy and adaptability.

The evolving mobile network specifications, as well as the increasingly stringent requirements for bandwidth, quality of service, and dynamic on-demand adaptability, push the boundaries of what is achievable with legacy networking technologies. Software-defined networking (SDN) and Network Functions Virtualization (NFV) provide agile, cost-efficient solutions by enabling an automated Service Management and Orchestration (SMO), allowing for dynamic resource allocation and network (re-)configuration.In this paper, we present an experimental demonstration of an SMO solution for virtualized mobile networks based on a seamless integration of an SDN-based transport network and an cloud computing environment. Our proposed SMO solution is evaluated by investigating the time required for establishing a virtual mobile core network service chain (i.e. a core network slice) on demand within an SDN-based cloud network considering its specific traffic and QoS requirements.

The increasing demand for determinism in modern industrial communication, driven by Industry 4.0, has led to the development of IEEE Time-Sensitive Networking (TSN) standards. However, integrating and configuring interconnected heterogeneous industrial networks remains a significant challenge. This paper extends the novel hierarchical Software-Defined Networking (SDN) based architectural design and control plane framework introduced in the context of orchestration and management of a multi-domain TSN network. We present relevant data models and a signaling schema essential for establishing end-to-end inter-domain time-sensitive streams within the proposed architecture. A proof-of-concept implementation validates the feasibility of the framework and demonstrates its performance advantages over the peer-to-peer model. The scalability of the framework for large-scale industrial networks is verified, and it ensures secure information encapsulation among domains, enabling seamless integration of multi-vendor heterogeneous applications. Furthermore, we investigate the use of CORECONF as a lightweight alternative to NETCONF for network management of multi-domain TSN network, providing experimental results.

The evolution of industrial communication to Industry 4.0 signifies the need for determinism in all aspects of communication. While the IEEE Time-Sensitive Networking (TSN) standards cater to these needs, the integration and configurability of such interconnected heterogeneous industrial networks remain an open research question. This paper describes a novel hierarchical Software-Defined Networking (SDN) based architectural design and control plane framework, introduced for the first time in the context of orchestration and management of a multi-domain TSN network. In addition to the architecture, we propose the relevant data models and signaling schema required to set up an end-to-end inter-domain time-sensitive stream. Proof-of-concept implementation validates the feasibility of the composed framework while bringing performance-wise significant benefits compared to its conceptual alternative, i.e., the peer-to-peer model. The proposed framework is found to be scalable and eliminates exposure of domain-specific information amongst domains, facilitating multi-vendor heterogeneous applications.

Botnets are categorized as one of the top 15 security threats by the European Union Agency for Cybersecurity (ENISA) in 2020. Thus, various Machine Learning (ML) algorithms have been proposed to fight against bot activities. Meanwhile, attackers constantly update their malware, making it more undetectable. Consequently, many proposed methods fail in detecting Cyber attacks from novel botnets. This paper presents a novel ensemble learning framework in which diverse standalone novelty-based learners are trained with legitimate traffic to detect previously unseen benign and anomalous traffic flows. Then, the predictions of these base learners are fed into an unsupervised neural network-based as an ensemble model to be combined. The results show that our proposed unsupervised ensemble learning framework yields an improved botnet detection performance (F1 Score=(0.9957 ± 0.0000) %) while minimizing false alarms. Also, our framework outperforms any contributing base learner and the baseline ensemble method, i.e., majority voting.

Fully disaggregated device deployments in optical networks propose to drive down network upgrade costs. These devices are managed by open-source control plane solutions for multi-vendor interoperability, which need to be tested in a simulation environment. We demonstrate a cloud-based solution which deploys 69 OpenROADM-based containerized optical networking elements, thereby simulating a nation-wide fully disaggregated optical transport network. Further, the planning, orchestration, and restoration of optical services can be decoupled from the simulated network, by using Transport Layer Security (TLS) enabled North-Bound REST APIs exposed by OpenDayLight TransportPCE, which is an open-source optical domain controller.

Industrie 4.0 reinforces advanced technology development to realize Time-Sensitive Networking (TSN) with real-time guarantees. The evolution of traditional industrial- automation systems such as Programmable Logic Controllers (PLCs) to cyber-physical systems by means of virtualization is one of the key enhancements. In this paper, we evaluate the impact of the placement of virtual PLCs (vPLCs) at edge clouds in conjunction with scheduling and routing of Time-Triggered (TT) traffic in 802.1Qbv-based industrial networks. We propose two approaches to solve this problem adopting a multi-objective optimization model. In the first approach, we solve separately the problem of placement and scheduling-routing by using a Mixed Integer Linear Programming (MILP) formulation. While in the second approach, we propose a Simulated Annealing (SA)- based meta-heuristic to solve the joint placement, scheduling, and routing optimization problem. We compare both the algorithms for real-world test cases and validate our solutions using the OMNeT++ simulator.

Reconfiguration of optical connections in flex-grid networks become increasingly necessary as the arrivals and departures of optical connection requests are more dynamic. For example, optical spectrum fragmentation can be alleviated by migrating existing connections so as to eliminate, align, and merge fragmented spectrum vacancies in order to improve request acceptance. To mitigate disruptions of existing lightpaths during migration, former research applies the concept of resource dependency digraph (RDD) to allow for sequential migration of optical connections. In this paper, we argue that with this RDD approach, each connection has a quota of one migration step only, and that this one-step-quota constraint can be safely lifted in order to accomplish an even lower disruption time due to the greater degree of migration freedom. We consider the whole reconfiguration process to be composed of multiple lightpath assignment states, where the transition between two consecutive states represents a migration step. The multiple lightpath assignment states constitute a progressive advancement from the current configuration towards the desired target configuration. The approach is named Multi-step Optical Connection Migration (MOCM) and is modeled as an integer-linear program (ILP). In addition, while former works restrict parallel migrations and adopt sequential migrations to mitigate connection disruptions, we show that more parallel migrations with the one-step-quota constraint lifted in MOCM can actually further minimize disruptions.

In this paper, we propose an adaptive intrusion response solution based on deep reinforcement learning, namely DeepAir, to effectively defend against cyber-attacks in Software-Defined Networks (SDN). Specifically, we first study an intrusion response system (IRS) that operates at the SDN control plane. Next, we propose a dynamic intrusion response solution to maximize the attack defense performance while minimizing the negative impact on benign traffic forwarding and the policy deployment cost in the SDN data plane. Then, we model the intrusion response system based on a Markov decision process (MDP) approach and formulate the related optimization problem. Afterward, we develop a Double Deep${Q}$-Network based intrusion response control algorithm to assist the intrusion response system to quickly obtain the optimal intrusion response policy. In our case study, we consider denial-of-service (DoS) attacks—the performance evaluation results demonstrate that DeepAir can effectively prevent malicious packets from arriving at the victim in all considered DoS attack scenarios, i.e., approximately 85% of attack packets are dropped. Moreover, by applying the optimal intrusion response policy, DeepAir can significantly reduce the ratio of Quality-of-Service violated traffic flows compared to a${Q}$-learning based approach (by 70%), and to two existing solutions, i.e., GATE (by 75%) and GTAC-IRS (by 80%), respectively.

In this paper, we propose a novel flow rule matching framework, DeepMatch, in Software-Defined Networking (SDN) to provide a fine-grained traffic flow measurement capability. Specifically, the flow rule matching control at a particular SDN switch is examined to maximize the traffic flow granularity degree while proactively protecting the flow-table in the switch from being overflowed. This control process is supervised by a control module referred to as DeepMatch instance. Regarding this instance, an optimization problem is formulated based on a Markov decision process (MDP) and a Partially Observable Markov decision process (POMDP), respectively. We develop a deep dueling neural network based flow rule matching control algorithm to solve the optimization problem, thereby quickly attaining a significant traffic flow granularity level and eliminating the switch flow-table overflow problem. Furthermore, we propose an experience data sharing (EDS) mechanism that enables a new instance to learn faster about the flow rule matching control. The results of our performance evaluation show that, by applying the DeepMatch framework in a highly dynamic traffic scenario, the traffic flow granularity degree at the access and the core switches increases by 24.0% and 31.63%, respectively, compared to the FlowStat method. DeepMatch is also highly outperforming the ReWiFlow, SDN-Mon, and Exact-Match approaches. In addition, by employing the EDS mechanism, a new instance can reduce its learning time up to 46.42% for supervising an access switch and up to 37.50% for supervising a core switch.

Reconfigurations of virtual networks in cloud environments are enabled by virtual machine (VM) migration technologies. A lot of research work has focused on the implementation of VM migration (such as pre/post-copy techniques) and the determination of the destination hosts for the VM placement. The latter is often related to virtual network embedding (VNE) because it entails the mapping of virtual network functions and traffic flows on the physical substrate. However, a lack of consideration of VNE in the research on VM migration (and vice versa) still prevails. We consider this joint problem and observe that when one regards this as a multi-step model, remarkable benefits in terms of migration time, migration traffic volume, and even migration feasibility are obtained. In this paper, we present a heuristic algorithm to accelerate the solution and show that high-quality solutions can be achieved in polynomial time with suitable parameter settings.

In 5G radio access networks, meeting the performance requirements of the fronthaul network is quite challenging. Recent standardization and research activities are focusing on exploiting the IEEE Time Sensitive Networking (TSN) technology for fronthaul networks. In this work we evaluate the performance of Ethernet TSN networks based on IEEE 802.1Qbv and IEEE 802.1Qbu for carrying real fronthaul traffic and benchmark it against Ethernet with Strict priority and Round Robin scheduling. We demonstrate that both 802.1Qbv and 802.1Qbu can be well used to protect high-priority traffic flows even in overload conditions.

The legacy security defense mechanisms cannot resist where emerging sophisticated threats such as zero-day and malware campaigns have profoundly changed the dimensions of cyber-attacks. Recent studies indicate that cyber threat intelligence plays a crucial role in implementing proactive defense operations. It provides a knowledge-sharing platform that not only increases security awareness and readiness but also enables the collaborative defense to diminish the effectiveness of potential attacks. In this paper, we propose a secure distributed model to facilitate cyber threat intelligence sharing among diverse participants. The proposed model uses blockchain technology to assure tamper-proof record-keeping and smart contracts to guarantee immutable logic. We use an open-source permissioned blockchain platform, Hyperledger Fabric, to implement the blockchain application. We also utilize the flexibility and management capabilities of Software-Defined Networking to be integrated with the proposed sharing platform to enhance defense perspectives against threats in the system. In the end, collaborative DDoS attack mitigation is taken as a case study to demonstrate our approach.

Software-Defined Networking (SDN) leverages the implementation of reliable, flexible and efficient network security mechanisms which make use of novel techniques such as artificial intelligence (AI) and machine learning (ML). In particular, these techniques - together with SDN - are the key enablers for the design of anomaly detection methods which are based on efficient traffic flow monitoring. In this paper, we tackle this problem by proposing an efficient anomaly detection framework, denoted as DeepGuard, which improves the detection performance of cyberattacks in SDN based networks by adopting a fine-grained traffic flow monitoring mechanism. Specifically, the proposed framework utilizes a deep reinforcement learning technique, i.e., Double Deep${Q}$-Network (DDQN), to learn traffic flow matching strategies maximizing the traffic flow granularity while proactively protecting the SDN data plane from being overloaded. Afterwards, by implementing the learned optimal traffic flow matching control policy, the most beneficial traffic information for anomaly detection is acquired at runtime—thereby improving the cyberattack detection performance. The performance of the proposed framework is validated by extensive experiments, and the results show that DeepGuard yields significant performance improvements compared to existing traffic flow matching mechanisms regarding the level of traffic flow granularity. In the case of distributed denial-of-service (DDoS) attacks, DeepGuard achieves a remarkable attack detection performance while effectively preventing forwarding performance degradation in the SDN data plane.

The following topics are dealt with: learning (artificial intelligence); telecommunication traffic; virtualisation; resource allocation; Internet of Things; software defined networking; Internet; computer network management; mobile computing; 5G mobile communication.

Software Defined Networking (SDN) enables flexible and scalable network control and management. However, it also introduces new vulnerabilities that can be exploited by attackers. In particular, low-rate and slow or stealthy Denial-of-Service (DoS) attacks are recently attracting attention from researchers because of their detection challenges. In this paper, we propose a novel machine learning based defense framework named Q-MIND, to effectively detect and mitigate stealthy DoS attacks in SDN-based networks. We first analyze the adversary model of stealthy DoS attacks, the related vulnerabilities in SDN-based networks and the key characteristics of stealthy DoS attacks. Next, we describe and analyze an anomaly detection system that uses a Reinforcement Learning-based approach based on Q-Learning in order to maximize its detection performance. Finally we outline the complete Q-MIND defense framework that incorporates the optimal policy derived from the Q- Learning agent to efficiently defeat stealthy DoS attacks in SDN-based networks. An extensive comparison of the Q-MIND framework and currently existing methods shows that significant improvements in attack detection and mitigation performance are obtained by Q-MIND.

In this paper, we introduce a novel optimisation model for virtual network functions (VNFs) migration in multiple steps. Since VNF migration can be enabled by VM migration, we model the course of VM migration by applying the concept of time-expanded networks. Our model is related to virtual network embedding (VNE), i.e., the problem of mapping virtual networks, or service chains (SCs), onto a capacitated substrate network. Contrary to the classical static VNE problem, we focus on finding an optimum transition from one mapping to another through one or multiple intermediate mappings. The concept of multi-step VM migration was first introduced in a previous publication, in which a fixed migration deadline per step was imposed. In this paper we remove this restriction, making the migration model more flexible. The VNF migration problem is formulated as a mixed integer linear program (MILP) and solved by a commercial solver. The performance of our new approach is evaluated via simulations assuming a realistic substrate network topology and SCs. The results show significant performance improvements w.r.t. migration time, failure sensitivity, feasibility, and cost of migration, especially in the case of high network utilisation.

Risk assessment comprises a series of processes for evaluating the extent to which a system is threatened by cyber-attacks. One important aspect of risk assessment is to determine the magnitude of impact of cyber-attacks. In this paper we propose a novel impact analysis approach which adopts both qualitative and quantitative elements for determining the impact value. As use case, we consider a Software-Defined Networking (SDN)-based communication system and three types of Distributed Denial of Service (DDoS) attacks, namely ICMP Flood, UDP Flood, and TCP Syn Flood attacks. By applying the impact analysis approach, we are able to calculate the impact of the aforementioned DDoS attack types on each network component and identify the most impacted component. We are also able to calculate the impact on the whole SDN network and thus, to figure out the most severe DDoS attack type.

Network Slicing is envisaged as a key component to address the challenges arising in next generation networks concerning the deployment, control and management of services. Besides, it promotes concurrent operation of multiple logical networks with diverging requirements on a common substrate platform. In this regard, the problem of designing individual logical network slices and mapping them onto the underlying substrate network gains significance. We denote this problem as the Network Slice Design Problem. In this work, we first consider the general network slice design problem. Adopting the robust optimisation approach of Bertsimas and Sim [1], [2], we then develop two additional formulations: i) to handle traffic demand uncertainties, and ii) to account for the correlations among the uncertain traffic demands. Finally, we present an extensive evaluation of the proposed formulations using realistic network instances.

This paper presents an evaluation and comparison of the infrastructure costs of five intra-data centre network architectures, namely, leaf-spine, fat-tree, hybrid fat-tree, Facebook 4-Post and a new Facebook fabric. A bottom-up cost calculation approach is used whereby all network architectures are designed so as to meet target oversubscription ratios. The resulting designs provide a list of materials that specify both the switching and cabling components required to build the networks. With this information the costs are evaluated from price books of network components available on the market. Different data centre sizes - defined by the number of physical servers housed in the data centre - are considered to investigate how the costs scale and compare across all network architectures. The results reveal that the economic savings brought in by each architecture depend on the data centre size. The most cost-efficient architecture is leaf-spine. The hybrid fat-tree also provides low costs for large-size data centres. In all considered scenarios it has been observed that the leaf (or top of the rack) switches are the dominant cost drivers.

5G networks are conceived to be highly flexible and programmable end-to-end connect-and-compute infrastructures. In that context, the concept of network slicing is of particular importance [1]. From a network infrastructure point of view, network slicing refers to the provisioning and assignment of physical substrate network resources to tenants. To enable an efficient resource allocation, suitable optimization models are required. In our previous contributions [2], we presented a model that takes into account traffic uncertainty by using the well known concept of Γ-robustness. We further extended this model to cope with general network slices and to consider also single substrate link and node failures [3]. In this paper, we present a novel model applying the concept of light robustness [4] [5] to address scalability issues of our previous models and to get a deeper insight into the tradeoff between the price of robustness and the realized robustness. We compare our model with a nominal and Γ-robust approach for different scenarios using network topology examples from SNDlib [6].

Techniques like Network Functions Virtualisation and Software Defined Networking provide a new dimension of flexibility in the deployment, operation and maintenance of telecommunication networks. They also enable the realisation of multiple virtual networks (multi-tenancy) on a common substrate network infrastructure. Provisioning such virtual networks requires efficient resource allocation mechanisms so that the utility of the substrate infrastructure provider can be maximised. In this work, we first outline a mathematical model for the general network slice design problem and extend it to cope with traffic uncertainties. We employ the Γ-robust uncertainty set [1], [2] to model the uncertainties in the traffic demands. Furthermore, we add survivability aspects to our model by protecting the network slice against single substrate network element (node/link) failures. Finally, both survivability and traffic robustness approaches are considered simultaneously and we present two different optimisation models. A performance evaluation is carried out comparing the different robust and survivable models with their non-robust non-survivable counterpart using network topology examples from SNDlib.

Wikipedia is one of the most popular information platforms on the Internet. The user access pattern to Wikipedia pages depends on their relevance in the current worldwide social discourse. We use publically available statistics about the top-1000 most popular pages on each day to estimate the efficiency of caches for support of the platform. While the data volumes are moderate, the main goal of Wikipedia caches is to reduce access times for page views and edits. We study the impact of most popular pages on the achievable cache hit rate in comparison to Zipf request distributions and we include daily dynamics in popularity.

This paper addresses an important aspect of mobile core network virtualization: The combined optimization of the virtual mobile core network topology (graph) and its embedding onto a physical substrate network. Basically this comprises the placement of mobile core virtual network functions (VNFs) onto the nodes of the physical substrate network, the determination of the interconnections towards the radio access network (RAN) and the Internet as well as the traffic routing between the VNFs. This problem differs from the traditional virtual network embedding (VNE) problem as the virtual network topology is not known in advance and several additional constraints apply, e.g. not every node of the physical substrate network might be able to host every VNF. We assume that the topology, link capacities and node resources of the physical substrate network are predefined and that a node comprises both packet forwarding and datacenter/server functionality. The node capabilities are defined by the processing, storage and switching (throughput) resources as well as the ability to host specific mobile core VNFs, i.e. the SGW, PGW, MME and HSS virtual functions. For the traffic routing, explicit single path routing is assumed. We propose a novel integer linear programming formulation which combines the optimization of the virtual network topology with VNE optimization. Optimization target is to minimize the cost of occupied link and node resources. Our formulation relies on the joint embedding of individual core network service chains where a core network service chain denotes the sequence of mobile core VNFs a user or control plane traffic flow traverses. We evaluate our model by means of two physical network topology examples taken from SNDlib [1]. It is shown that our approach outperforms traditional VNE optimization approaches in terms of optimality and computation time.

In this paper we present a routing protocol (Modified HWMP, MHWMP) for IEEE 802.11s WLAN mesh networks with smart antennas that incorporates the optimum selection of the PHY-layer transmission/reception mode (multiplexing, beamforming and diversity). MHWMP adaptively selects between spatial multiplexing and beamforming for data transmission according to the wireless channel conditions. We also modified the traditional RTS/CTS mechanism to take into account the different behaviors of both transmission modes at MAC layer: the advantages of diversity are harvested by sending RTS/CTS frames with space time block coding in case of beamforming while standard RTS/CTS frames are used in case of spatial multiplexing. Simulation results illustrate that MHWMP leads to significant better throughput and delay performance in certain situations. Moreover, it enables a high degree of robustness wrt. wireless link failures for stationary mesh networks.

Significant cost reductions can be achieved through the deployment of alternative homing architectures. These reductions, however, come at the expense of increased recovery time. In this paper we focus on the dual homing with shared backup router resources (SBRR) architecture and propose an analytical recovery time model based on GMPLS signaling. Case studies are presented showing recovery times ranging from 100 ms to around 2500 ms. Simultaneously, the dominating time contributing factors are identified, while the impact of varying parameters such as the network edge length is quantified. We proceed to show how service-imposed maximum outage requirements have a direct effect on the configuration of the SBRR architecture.