This paper introduces a novel optimization framework for Network Functions Virtualization (NFV) that addresses the efficient implementation of end-to-end service requests in physical networks. Our approach characterizes each server node by a reliability function reflecting its computational load, which aids in balancing workloads and mitigating congestion. By optimizing the reliability metric along the route, our approach ensures robust end-to-end service quality. We formulate the NFV deployment problem as a non-convex mixed-integer non-linear programming (MINLP) model aimed at minimizing both deployment and operational costs while maximizing resource utilization, addressing also per-node installation conflicts and inter-VNF incompatibilies. Given the NP-hard nature of the problem, we develop efficient linearization techniques and bounding schemes, using also dynamic programming, to convert the formulation into a tractable mixed-integer linear programming (MILP) model. Additionally, a cutting-plane-based heuristic with a warm-start strategy is proposed to further accelerate convergence. Experimental evaluations on real-world network topologies demonstrate that our framework offers scalable and cost-effective solutions compared to existing approaches.

We investigate the problem of resilient and energy-aware Virtual Network Function (VNF) placement and routing in softwarized networks under the threat of targeted cyberattacks. We model the system as a bilevel interdiction game, where a malicious attacker strategically disrupts servers within a fixed resource budget, while a network provider reacts by minimizing energy consumption through optimized VNF deployment and flow routing. The lower-level problem includes capacity constraints, service function chaining, and a server energy model accounting for idle and load-dependent consumption. Attack-induced load shifts are captured via additive energy penalties on compromised nodes. To solve this inherently difficult bilevel integer program, we de-velop a single-level reformulation via interdiction cuts and propose a cutting-plane algorithm to explore the attacker's strategy space efficiently. Numerical experiments show the effectiveness of the approach in quantifying trade-offs between resilience and energy efficiency, supporting trustworthy and adaptive NFV deployment in critical infrastructures.

This paper introduces a novel optimization framework for Network Functions Virtualization (NFV) that addresses the efficient implementation of end-to-end service requests in physical networks. Our approach characterizes each server node by a reliability function reflecting its computational load, which aids in balancing workloads and mitigating congestion. By optimizing the reliability metrics along the route, our approach ensures robust end-to-end service quality. We formulate the NFV deployment problem as a non-convex mixed-integer non-linear programming (MINLP) model aimed at minimizing both deployment and operational costs while maximizing resource utilization. Given the NP-hard nature of the problem, we develop efficient linearization techniques and bounding schemes, using also dynamic programming, to convert the formulation into a tractable mixed-integer linear programming (MILP) model. Additionally, a cutting-plane-based heuristic with a warm-start strategy is proposed to further accelerate convergence. Experimental evaluations on real-world network topologies demonstrate that our framework offers scalable and cost-effective solutions compared to existing approaches.

Cellular networks worldwide are currently experiencing a significant surge in service demand, forcing operators to focus on the accurate modeling of network dynamics as a key task to enhance efficiency. Besides being useful for optimizing network functioning, mobile data analytics have unleashed unforeseen opportunities to address several social and urban issues on a large scale. In this work, we seize such opportunities and propose a framework capable of profiling urban settlements based on the interplay between their attractiveness and the characteristics of the built environment. Focusing on the impact of the COVID-19 pandemic on mobile users’ behavior, we conduct a comprehensive case study in Italy. Leveraging real-world mobile radio access data, we investigate the spatial variations in people’s visiting patterns, providing insights into how these changes correlate with the social and urban context characterizing the reference area.

Mobile radio networks have been evolving towards the integration of services and devices with a diverse set of throughput, latency, and reliability requirements. To support these requirements, 3GPP has introduced Multi Connectivity (MC) as a more flexible architecture for 5G New Radio (NR), where multiple radio links can be simultaneously activated to split or duplicate data traffic. Multi connectivity improves single user performance at the cost of higher interference due to the increase of radio transmissions, which negatively affects system throughput. This paper analyzes the problem of admission control and resource allocation in multi connectivity scenarios, considering different requirements and 5G NR features. Specifically, we formulate two optimization problems that leverage the features of the Packet Data Convergence Protocol (PDCP) layer, which controls the flow of data packets of the data radio bearer: the PDCP Split-Bearer Decision (PSD) and the PDCP Duplication Decision (PDD) problems, which are tailored for the enhanced Mobile Broadband (eMBB) and Ultra Reliable Low Latency Communications (uRLLC) services, respectively. We further provide heuristic approaches, specifically designed for the PSD and PDD problems, to effectively solve both these problems. Numerical results in realistic network deployments confirm that our solutions can effectively allocate radio resources increasing admission rate and system throughput, while guaranteeing the required reliability level.

Multi-access Edge Computing (MEC) facilitates the deployment of critical applications with stringent QoS requirements, latency in particular. This article considers the problem of jointly planning the availability of computational resources at the edge, the slicing of mobile network and edge computation resources, and the routing of heterogeneous traffic types to the various slices. These aspects are intertwined and must be addressed together to provide the desired QoS to all mobile users and traffic types still keeping costs under control. We formulate our problem as a mixed-integer nonlinear program (MINLP) and we define a heuristic, named Neighbor Exploration and Sequential Fixing (NESF), to facilitate the solution of the problem. The approach allows network operators to fine tune the network operation cost and the total latency experienced by users. We evaluate the performance of the proposed model and heuristic against two natural greedy approaches. We show the impact of the variation of all the considered parameters (viz., different types of traffic, tolerable latency, network topology and bandwidth, computation and link capacity) on the defined model. Numerical results demonstrate that NESF is very effective, achieving near-optimal planning and resource allocation solutions in a very short computing time even for large-scale network scenarios.

Software-Defined Networking (SDN) is becoming the reference paradigm to provide advanced Traffic Engineering (TE) solutions for future networks. However, taking all TE decisions at the controller, in a centralized fashion, may require long delays to react to network changes. With the most recent advancements in SDN programmability some decisions can (and should indeed) be offloaded to switches.In this paper we present a model to route elastic demands in a general network topology adopting a semi-distributed approach of the control plane to deal with path congestion. Specifically, we envision a Stackelberg approach where the SDN controller takes the role of Leader, choosing the most appropriate subset of routing paths for the selfish users (network switches), which behave as Followers, making local routing decisions based on path congestion. To overcome the complexity of the problem and meet the time requirements of real-life settings, we propose effective heuristic procedures which take into accurate account traffic dynamics, considering a stochastic scenario where both the number and size of flows change over time. We test our framework with a custom-developed simulator in different network topologies and instance sizes. Numerical results show how our model and heuristics achieve the desired balance between making global decisions and reacting rapidly to congestion events.

Mobile Edge Computing (MEC) is a key technology for the deployment of next generation (5G and beyond) mobile networks, specifically for reducing the latency experienced by mobile users which require ultra-low latency, high bandwidth, as well as real-time access to the radio network. In this paper, we propose an optimization framework that considers several key aspects of the resource allocation problem for MEC, by carefully modeling and optimizing the allocation of network resources including computation and storage capacity available on network nodes as well as link capacity. Specifically, both an exact optimization model and an effective heuristic are provided, jointly optimizing (1) the connections admission decision (2) their scheduling, also called calendaring (3) and routing as well as (4) the decision of which nodes will serve such connections and (5) the amount of processing and storage capacity reserved on the chosen nodes. Numerical experiments are conducted in several real-size network scenarios, which demonstrate that the heuristic performs close to the optimum in all the considered network scenarios, while exhibiting a low computing time.

In this paper, we propose a truthful combinatorial auction for the joint radio and processing resource allocation problem in the context of a Cloud-based Radio Access Network (C-RAN). We formulate the auction as an Integer Linear Program (ILP), taking into accurate account interference constraints while leveraging radio resource reuse to generate an optimal revenue for the RAN operator. Then, we propose Truthful Greedy Approach (TGA), an effective and truthful heuristic that guarantees a close-to-optimum revenue compared to the one obtained with the ILP formulation. Extensive simulations, conducted in representative network scenarios, compare and evaluate our auction with state-of-the-art approaches from the literature, showing its effectiveness.

Mobile traffic generated by a variety of services is rapidly increasing in volume. Both network and computation resources in a single edge network are therefore often too limited to provide the desired Quality of Service (QoS) to mobile users. In this paper, we propose a mathematical model, called JSNC, to perform an efficient joint slicing of mobile network and edge computation resources. JSNC aims at minimizing the total latency of transmitting, outsourcing and processing user traffic, under the constraint of user tolerable latency for multiple classes of traffic. The constraints of network, link and server capacities are considered as well. The optimization model results in a mixed-integer nonlinear programming (MINLP) problem. To tackle it efficiently, we perform an equivalent reformulation, and based on that, we further propose two effective heuristics: Sequential Fixing (SF), which can achieve near-optimal solutions, and a greedy approach which obtains suboptimal results with respect to SF. Both of them can solve the optimization problem in a very short computing time. We evaluate the performance of the proposed model and heuristics, showing the impact of all the considered parameters (viz. different types of traffic, tolerable latency, network topology and bandwidth, computation and link capacity) on the optimal and approximate solutions. Numerical results demonstrate that JSNC and the heuristics can provide efficient resource allocation solutions.

The explosive growth of smartphones and other portable devices, along with new traffic types generated by M2M applications, are creating huge volumes of mobile data traffic and signaling overhead, therefore requiring a radical change to the current mobile network architecture. This has promoted new virtualization paradigms, which combine diverse packet core services, and provide network functions implemented in software, rather than in dedicated hardware appliances, in order to scale capacity and introduce new services in a fast and cost-effective way. In this paper1we study the optimization and resource allocation problems taking into account the deployment of virtualization structures. Our aim is to develop a theoretical framework of resource orchestration for mobile access networks, deriving the fundamental performance limits as well as the tradeoffs among the key system parameters. We therefore study optimal, time-varying placement and chaining of network functions. With respect to existing works, our optimization framework provides a much more precise system modeling, with, among others, a separation between control and data plane functions. We perform an extensive numerical analysis using both real traffic traces provided by a mobile operator (Vodafone UK) and real positions for radio access points for the UK area, and discuss the impact of network parameters on the system performance. Numerical results show that our proposed optimization framework permits to carefully model key aspects of network virtualization and service deployment/chaining in such scenarios, thus representing a very promising framework for the design of efficient and cost-effective mobile networks.

Network Functions Virtualization (NFV) has recently gained momentum among network operators as a means to share their physical infrastructure among virtual operators, which can independently compose and configure their communication services. However, the spatio-temporal correlation of traffic demands and computational loads can result in high congestion and low network performance for virtual operators, thus leading to service level agreement breaches. In this paper, we analyze the congestion resulting from the sharing of the physical infrastructure and propose innovative orchestration mechanisms based on both centralized and distributed approaches, aimed at unleashing the potential of the NFV technology. In particular, we first formulate the network functions composition problem as a non-linear optimization model to accurately capture the congestion of physical resources. To further simplify the network management, we also propose a dynamic pricing strategy of network resources, proving that the resulting system achieves a stable equilibrium in a completely distributed fashion, even when all virtual operators independently select their best network configuration. Numerical results show that the proposed approaches consistently reduce resource congestion. Furthermore, the distributed solution well approaches the performance that can be achieved using a centralized network orchestration system.

Albeit an important goal of caching is traffic reduction, a perhaps even more important aspect follows from the above achievement: the reduction of internet service provider (ISP) operational costs that comes as a consequence of the reduced load on transit and provider links. Surprisingly, to date this crucial aspect has not been properly taken into account in cache design. In this paper, we show that the classic caching efficiency indicator, i.e., the hit ratio, conflicts with cost. We therefore propose a mechanism whose goal is the reduction of cost and, in particular, we design a cost-aware (CoA) cache decision policy that, leveraging price heterogeneity among external links, tends to store with more probability the objects that the ISP has to retrieve through the most expensive links. We provide a model of our mechanism, based on Che's approximation, and, by means of a thorough simulation campaign, we contrast it with traditional cost-blind schemes, showing that CoA yields a significant cost saving, that is furthermore consistent over a wide range of scenarios. We show that CoA is easy to implement and robust, making the proposal of practical relevance.

Demand-Side Management (DSM) systems represent an efficient method to improve the performance of Smart Grid infrastructures by controlling users' power loads. In this paper, we focus our analysis on fully distributed DSM systems especially designed to reduce the peak demand of groups of residential users. In our proposed scheme, each appliance decides autonomously its scheduling using only limited information on the energy price fixed by the retailer, thus greatly reducing the system complexity as well as the need of information exchanges. We develop two schedule-selection policies based on the Proportional Imitation Rule, where at each iteration all appliances switch to a new schedule with a probability proportional to the cost difference between the actual and cheapest schedules of the previous iteration. We analyze the proposed learning methods based on realistic instances in several use-case scenarios, and show their effectiveness in terms of cost reductions (both local and system-wide) as well as convergence speed to stable and efficient system equilibria.

The opportunistic utilization of third party WiFi access devices to offload customer traffic from the mobile network has recently gained momentum as a promising approach to increase the network capacity and simultaneously reduce the energy consumption of the radio access network (RAN) infrastructure. To foster the opportunistic utilization of unexploited Internet connections, we propose a new and open market where a mobile operator can lease the bandwidth made available by third parties (residential users or private companies) through their access points to increase dynamically (and adaptively) the network capacity. We formulate the offloading problem as a reverse auction considering the most general case of partial covering of the traffic to be offloaded. We discuss the conditions (i) to offload the maximum amount of data traffic according to the capacity made available by third party access devices, (ii) to foster the participation of access point owners (individual rationality), and (iii) to prevent market manipulation (incentive compatibility). Finally, we propose three alternative greedy algorithms that efficiently solve the offloading problem, even for large-size network scenarios.

Nowadays, the maintenance costs of wireless devices represent one of the main limitations to the deployment of wireless mesh networks (WMNs) as a means to provide Internet access in urban and rural areas. A promising solution to this issue is to let the WMN operator lease its available bandwidth to a subset of customers, forming a wireless mesh community network, in order to increase network coverage and the number of residential users it can serve. In this paper, we propose and analyze an innovative marketplace to allocate the available bandwidth of a WMN operator to those customers who are willing to pay the higher price for the requested bandwidth, which in turn can be subleased to other residential users. We formulate the allocation mechanism as a combinatorial truthful auction considering the key features of wireless multihop networks and further present a greedy algorithm that finds efficient and fair allocations even for large-scale, real scenarios while maintaining the truthfulness property. Numerical results show that the greedy algorithm represents an efficient, fair, and practical alternative to the combinatorial auction mechanism.

The timing channel is a logical communication channel in which information is encoded in the timing between events. Recently, the use of the timing channel has been proposed as a countermeasure to reactive jamming attacks performed by an energy-constrained malicious node. In fact, while a jammer is able to disrupt the information contained in the attacked packets, timing information cannot be jammed, and therefore, timing channels can be exploited to deliver information to the receiver even on a jammed channel. Since the nodes under attack and the jammer have conflicting interests, their interactions can be modeled by means of game theory. Accordingly, in this paper, a game-theoretic model of the interactions between nodes exploiting the timing channel to achieve resilience to jamming attacks and a jammer is derived and analyzed. More specifically, the Nash equilibrium is studied in terms of existence, uniqueness, and convergence under best response dynamics. Furthermore, the case in which the communication nodes set their strategy and the jammer reacts accordingly is modeled and analyzed as a Stackelberg game, by considering both perfect and imperfect knowledge of the jammer's utility function. Extensive numerical results are presented, showing the impact of network parameters on the system performance.

Caching is frequently used by Internet Service Providers as a viable technique to reduce the latency perceived by end users, while jointly offloading network traffic. While the cache hit-ratio is generally considered in the literature as the dominant performance metric for such type of systems, in this paper we argue that a critical missing piece has so far been neglected. Adopting a radically different perspective, in this paper we explicitly account for the cost of content retrieval, i.e. the cost associated to the external bandwidth needed by an ISP to retrieve the contents requested by its customers. Interestingly, we discover that classical cache provisioning techniques that maximize cache efficiency (i.e., the hit-ratio), lead to suboptimal solutions with higher overall cost. To show this mismatch, we propose two optimization models that either minimize the overall costs or maximize the hit-ratio, jointly providing cache sizing, object placement and path selection. We formulate a polynomial-time greedy algorithm to solve the two problems and analytically prove its optimality. We provide numerical results and show that significant cost savings are attainable via a cost-aware design.

Information-Centric Networking (ICN) has recently gained momentum as a promising paradigm for the next-generation Internet architecture. The first prototypes for ICN-capable routers have already been developed, and network operators will soon have the opportunity to experience the advantages introduced by this technology. However, to migrate the devices to this novel architecture, non-negligible investments should be made. Therefore, it is of utter importance to provide clear quantitative insights of the expected economic benefits that operators will experience by switching to the ICN paradigm. For these reasons, in this paper we tackle the content-aware network-planning problem, and we formulate a novel optimization model to study the migration to an ICN, in a budget-constrained scenario. Our formulation takes into account 1) traffic routing and 2) content caching. We further complement our contribution by designing a Randomized Rounding heuristic that scales up to realistic topologies composed of hundreds of nodes.

Virtualization of network functions and services can significantly reduce capital and operational expenditures of telecommunication operators through the sharing of a single network infrastructure. However, the utilization of the same resources can increase their congestion due to the spatio-temporal correlation of traffic demands and computational loads. In this paper, we propose novel orchestration mechanisms to optimally control and reduce the resource congestion of a physical infrastructure based on the NFV paradigm. In particular, we formulate the network functions composition problem as a nonlinear optimization model to accurately capture the congestion of the physical resources. In order to meet both efficiency and load balancing goals of the physical operator, we introduce two variants of such model to minimize the total and the maximum congestion in the network. Our models allow us to efficiently compute the optimal solution in a short computing time. Numerical results, obtained with real ISP topologies and network instances, show that the proposed approach represents an efficient and practical solution to control the congestion in virtual networks. Furthermore, they indicate that a holistic approach that optimizes the virtual system by jointly considering all elements/components would further improve the performance.

Content Delivery Networks (CDNs) have been identified as one of the relevant use cases where the emerging paradigm of Network Functions Virtualization (NFV) will likely be beneficial. In fact, virtualization fosters flexibility, since on-demand resource allocation of virtual CDN nodes can accommodate sudden traffic demand changes. However, there are cases where physical appliances should still be preferred, therefore we envision a mixed architecture in between these two solutions, capable to exploit the advantages of both of them. Motivated by these reasons, in this paper we formulate a two-stage stochastic planning model that can be used by CDN operators to compute the optimal long-term network planning decision, deploying physical CDN appliances in the network and/or leasing resources for virtual CDN nodes in data centers. Key findings demonstrate that for a large range of pricing options and traffic profiles, NFV can significantly save network costs spent by the operator to provide the content distribution service.

The Content-Centric Networking paradigm aims at improving the Quality of Service of the Internet by providing innovative features to better handle digital content distribution. A major step towards the success of this novel paradigm is to analyze and compare its performance with respect to the most popular ways in which content is disseminated in today's IP Internet. In this paper we give clear answers to this critical issue by proposing a methodology to assess how the innovative design of Content-Centric Networking behaves as opposed to the solution proposed by Content-Distribution Networks. We develop a novel optimization model to study the performance bounds of a Content-Centric Network, by addressing the joint object placement and routing problem. We further introduce three comparative models that well describe 1) a Content-Distribution Network, 2) a traditional IP-based network, and 3) a Content-Centric Network whose caches are pre-populated with given contents. To the best of our knowledge, our proposal is the first that studies the performance bounds of Content-Centric Networks by means of an optimization model. Finally, we discuss the numerical results showing the performance bounds of this revolutionary paradigm. We discover that: 1) a Content-Centric Network with small caches can provide significant performance gains compared to a traditional IP-based network; 2) for large amounts of caching storage, the benefits of using sophisticated cache replacement policies are dramatically reduced and 3) in some scenarios, a Content-Distribution Network with few replica servers can perform better than a Content-Centric Network, even when the total amount of available caching storage is exactly the same.

Information Centric Networking (ICN) is a novel paradigm that aims at improving the performance of today's Internet by supporting universal caching and multicast content delivery features on every network device.

The Radio Access Network (RAN) infrastructure represents the most critical part for capacity planning, which usually accounts for peak traffic conditions. A promising approach to increase the RAN capacity and simultaneously reduce its energy consumption is represented by the opportunistic utilization of third party Wi-Fi access devices. In order to foster the utilization of unexploited Internet connections, we propose a new and open market, where a mobile operator can lease the bandwidth made available by third parties (residential users or private companies) through their access points to increase the network capacity and save large amounts of energy. We formulate the offloading problem as a reverse auction considering the most general case of partial covering of the traffic to be offloaded. We discuss the conditions (i) to offload the maximum amount of data traffic according to the capacity of third party access devices, (ii) to foster the participation of access point owners (individual rationality), and (iii) to prevent market manipulation (incentive compatibility). Finally, we propose a greedy algorithm that solves the offloading problem in polynomial time, even for large-size network scenarios.

Wireless mesh networks (WMNs) have emerged as a flexible and low-cost network infrastructure, where heterogeneous mesh routers managed by different users collaborate to extend network coverage. This paper proposes a novel routing metric, Expected Forwarded Counter (EFW), and two further variants, to cope with the problem of selfish behavior (i.e., packet dropping) of mesh routers in a WMN. EFW combines, in a cross-layer fashion, routing-layer observations of forwarding behavior with MAC-layer measurements of wireless link quality to select the most reliable and high-performance path. We evaluate the proposed metrics both through simulations and real-life deployments on two different wireless testbeds, performing a comparative analysis with On-Demand Secure Byzantine Resilient Routing (ODSBR) Protocol and Expected Transmission Counter (ETX). The results show that our cross-layer metrics accurately capture the path reliability and considerably increase the WMN performance, even when a high percentage of network nodes misbehave.

In recent years, with the evolution of new and content-rich Internet services, mobile network operators face the challenging task to guarantee ubiquitous access to their customers, while minimizing network deployment costs. In order to foster the opportunistic utilization of unexploited Internet connections of residential users, we propose a new marketplace where mobile network operators can rent the unused capacity of residential users' access devices (e.g., wireless access points or femtocells) when the traffic demand of their mobile customers exceeds the operator's network capacity. We formulate the allocation problem as a combinatorial reverse auction, which prevents market manipulation, and we further propose a greedy algorithm that finds efficient allocations in polynomial time, even for large-size network scenarios. Numerical results demonstrate that our proposed schemes well capture the economical and networking essence of the allocation problem, thus representing a promising approach to enhance the performance of next-generation wireless access networks.

Wireless mesh networks (WMNs) have emerged as a flexible and low-cost network infrastructure, where heterogeneous mesh routers managed by different users collaborate to extend network coverage. Several routing protocols have been proposed to improve the packet delivery rate based on enhanced metrics that capture the wireless link quality. However, these metrics do not take into account that some participants can exhibit selfish behavior by selectively dropping packets sent by other mesh routers in order to prioritize their own traffic and increase their network utilization. This paper proposes a novel routing metric to cope with the problem of selfish behavior (i.e., packet dropping) of mesh routers in a WMN. Our solution combines, in a cross-layer fashion, routing-layer observations of forwarding behavior with MAC-layer measurements of wireless link quality to select the most reliable and high-performance path. We integrated the proposed metric with a well-known routing protocol for wireless mesh networks, OLSR, and evaluated it using the NS2 simulator. The results show that our cross-layer metric accurately captures the path reliability, even when a high percentage of network nodes misbehave, thus considerably increasing the WMN performance.

Abstract Wireless Mesh Networks (WMNs) have emerged recently as a technology for next‐generation wireless networking. They consist of mesh routers and clients, where mesh routers are almost static and form the backbone of WMNs. WMNs provide network access for both mesh and conventional clients. In this paper, we propose DSA‐Mesh, a fully distributed security architecture that provides access control for mesh routers as well as a key distribution scheme that supports layer‐2 encryption to ensure security and data confidentiality of all communications that occur in the backbone of the WMN. DSA‐Mesh exploits the routing capabilities of mesh routers: after connecting to the access network as generic wireless clients, new mesh routers authenticate to a key management service (consisting of several servers) implemented using threshold cryptography, and obtain a temporary key that is used both to prove their credentials to neighbor nodes and to encrypt all the traffic transmitted on wireless backbone links. A key feature in the design of DSA‐Mesh is its independence from the underlying wireless technology used by network nodes to form the backbone. Furthermore, DSA‐Mesh enables seamless mobility of mesh routers. Since it is completely distributed, DSA‐Mesh permits to deploy automatically and incrementally large WMNs, while increasing, at the same time, the robustness of the system by eliminating the single point of failure typical of centralized architectures. DSA‐Mesh has been implemented in Network Simulator, and extensive simulations have been performed in large‐scale network scenarios, comparing it to a static key approach and to a centralized architecture where a single key server is deployed. Numerical results show that our proposed architecture considerably increases the WMN security and reliability, with a negligible impact on the network performance, thus representing an effective solution for wireless mesh networking. Copyright © 2010 John Wiley & Sons, Ltd.

Abstract Wireless Mesh Networks (WMNs) have emerged recently as a technology for providing high‐speed last mile connectivity in next‐generation wireless networks. Several MAC protocols that exploit multiple channels and directional antennas have been proposed in the literature to increase the performance of WMNs. However, while these techniques can improve the wireless medium utilization by reducing radio interference and the impact of the exposed nodes problem, they can also exacerbate the hidden nodes problem. Therefore, efficient MAC protocols need to be carefully designed to fully exploit the features offered by multiple channels and directional antennas. In this paper we propose a novel Multi‐Channel Power‐Controlled Directional MAC protocol (MPCD‐MAC) for nodes equipped with multiple network interfaces and directional antennas. MPCD‐MAC uses the standard RTS‐CTS‐DATA‐ACK exchange procedure. The novel difference is the transmission of the RTS and CTS packets in all directions on a separate control channel, while the DATA and ACK packets are transmitted only directionally on an available data channel at the minimum required power, taking into account the interference generated on already active connections. This solution spreads the information on wireless medium reservation (RTS/CTS) to the largest set of neighbors, while data transfers take place directionally on separate channels to increase spatial reuse. Furthermore, power control is used to limit the interference produced over active nodes. We measure the performance of MPCD‐MAC by simulation of several realistic network scenarios, and we compare it with other approaches proposed in the literature. The results show that our scheme increases considerably both the total traffic accepted by the network and the fairness among competing connections. Copyright © 2010 John Wiley & Sons, Ltd.

Efficient dynamic resource provisioning algorithms are necessary to the development and automation of Quality of Service (QoS) networks. The main goal of these algorithms is to offer services that satisfy the QoS requirements of individual users while guaranteeing at the same time an efficient utilization of network resources. This paper proposes a novel game theoretical model that solves the joint problem of non-cooperative QoS routing and dynamic capacity allocation in a parallel links network. Two categories of players are introduced: (1) the capacity players that dimension the link capacities to provide QoS guarantees to users, minimizing, at the same time, the links' congestion, and (2) network users, which are characterized by elastic traffic demands and split their traffic over multiple links, maximizing their objective function. This game is modeled as a multi-leader-follower game, where capacity players are leaders and network users are followers. We derive optimal routing and capacity settings using a round robin greedy algorithm, discussing numerical examples that provide insights into the model's solution.

This paper studies the economic interactions between Secondary Users and Primary Operators in a Cognitive Radio Network scenario. Secondary Users transmit their traffic, eventually splitting it over multiple available frequency spectra, each owned by an independent primary network operator. Users are charged a fixed price per unit of bandwidth used, and face spectrum access costs. The transmission rate of each secondary user is assumed to be function of network congestion (like for TCP traffic) and the price per bandwidth unit. Primary operators sell spare bandwidth to secondary users, and set spectrum access prices to maximize their revenue. We provide sufficient conditions for the existence and uniqueness of the Nash equilibrium considering a peculiar class of spectrum pricing functions, viz. polynomial functions, which lead to efficient spectrum allocation, and we derive optimal price and spectrum allocation settings. Finally, we discuss numerical cognitive radio network examples that provide insights into the model's solution.

In many scenarios network design is not enforced by a central authority, but arises from the interactions of several self-interested agents. This is the case of the Internet, where connectivity is due to Autonomous Systems' choices, but also of overlay networks, where each user client can decide the set of connections to establish. Recent works have used game theory, and in particular the concept of Nash Equilibrium, to characterize stable networks created by a set of selfish agents. The majority of these works assume that users are completely non-cooperative, leading, in most cases, to inefficient equilibria. To improve efficiency, in this paper we propose two novel socially-aware network design games. In the first game we incorporate a socially-aware component in the users' utility functions, while in the second game we use additionally a Stackelberg (leader-follower) approach, where a leader (e.g., the network administrator) architects the desired network buying an appropriate subset of network's links, driving in this way the users to overall efficient Nash equilibria. We provide bounds on the Price of Anarchy and other efficiency measures, and study the performance of the proposed schemes in several network scenarios, including realistic topologies where players build an overlay on top of real Internet Service Provider networks. Numerical results demonstrate that (1) introducing some incentives to make users more sociallyaware is an effective solution to achieve stable and efficient networks in a distributed way, and (2) the proposed Stackelberg approach permits to achieve dramatic performance improvements, designing almost always the socially optimal network.

Wireless Mesh Networks (WMNs) have recently emerged as a technology for next-generation wireless networking. WMNs partially replace wired backbone networks, and it is therefore reasonable to plan carefully radio resource assignment to provide quality guarantees to traffic flows. Directional transmissions allow to reduce radio interference, thus exploiting spatial reuse. Therefore, as a main contribution, in this paper we study the joint routing and scheduling optimization problem in Wireless Mesh Networks where nodes are equipped with directional antennas. To this aim, we assume a Spatial reuse Time Division Multiple Access (STDMA) scheme, a dynamic power control able to vary the emitted power slot-by-slot, and a rate adaptation mechanism that sets transmission rates according to the Signal- to-Interference-and-Noise Ratio (SINR). We provide column generation-based heuristic approaches for the proposed models in a set of realistic-size instances and discuss the impact of different parameters on the network performance. The results show that our schemes increase considerably the total traffic accepted by the network, providing bounds to the achievable performance.

Service overlay networks (SONs) can provide end-to-end quality of service guarantees in the Internet without requiring significant changes to the underlying network infrastructure. A SON is an application-layer network operated by a third-party Internet service provider (ISP) that owns a set of overlay nodes, residing in the underlying ISP domains, interconnected by overlay links. The deployment of a SON can be a capital-intensive investment, and hence its planning requires careful decisions, including the overlay nodes' placement, the capacity provisioning of overlay links as well as of access links that connect the end-users to the SON infrastructure. In this paper, we propose two novel optimization models for the planning of SONs. The first model minimizes the SON installation cost while providing full coverage to all network's users. The second model maximizes the SON operator's profit by further choosing which users to serve, based on the expected gain, and taking into consideration budget constraints. We also introduce two efficient heuristics to get near-optimal solutions for largescale instances in a reasonable computation time. We provide numerical results of the proposed models and heuristics on a set of realistic-size instances, and discuss the effect of different parameters on the characteristics of the planned networks. We show that in the considered network scenarios the proposed heuristics perform close to the optimum with a short computing time.

One of the major concerns of optical network operators is related to improving the availability of services provided to their highest-class clients through the use of different protection schemes. However, the majority of the work concerning protection schemes considered the primary connections as equally important when contending for the use of the backup resources. As a first contribution we therefore propose an improvement of the existing shared protection schemes through the introduction of relative priorities among the different primary connections contending for the access to the protection path. Moreover, as a second contribution, we propose to include a novel service differentiation parameter, the service disruption rate of a connection, to provide differentiated services in a WDM mesh network, and we motivate the use of such a parameter with numerical examples. As a third contribution, we present a mathematical model for both the classical protection schemes and for the proposed priority-aware scheme. As a key distinguishing feature from existing literature we derive explicit analytic expressions for the average availability and service disruption rate resulting from the deployment of such schemes. By solving these models we then evaluate numerically the benefits of the service differentiation feature introduced in our scheme as well as the impact of the service disruption rate as service differentiator.

Finding a path in the network for each traffic flow able to guarantee some quality parameters such as bandwidth and delay is the task of QoS routing algorithms developed for new IP networks based on label forwarding techniques as Multiprotocol Label Switching (MPLS). In this paper we focus on Dynamic QoS Routing, i.e. the routing of bandwidth guaranteed flows in a dynamic scenario where new connection requests arrive at the network edge nodes. When more than one path satisfying the bandwidth demand exists, the selection of the path aims at minimizing the blocking probability of future requests. We propose two novel mathematical programming models that assume the knowledge of arrival times and durations of connection requests, and provide theoretical bounds to the performance achievable by on-line routing algorithms. We compare to such bounds the performance of the Min-Hop (MH) algorithm, the Minimum Interference Routing Algorithm (MIRA) and the recently proposed Virtual Flow Deviation (VFD) algorithm. We show that the blocking probability of this new algorithm, in most scenarios, is quite close to the bound.

The use of enhanced bandwidth estimation procedures within the congestion control scheme of TCP was proposed recently as a way of improving TCP performance over links affected by random loss. This paper first analyzes the problems faced by every bandwidth estimation algorithm implemented at the sender side of a TCP connection. Some proposed estimation algorithms are then reviewed, analyzing and comparing their estimation accuracy and performance. As existing algorithms are poor in bandwidth estimation, and in sharing network resources fairly, we propose TIBET (time intervals based bandwidth estimation technique). This is a new bandwidth estimation scheme that can be implemented within the TCP congestion control procedure, modifying only the sender-side of a connection. The use of TIBET enhances TCP source performance over wireless links. The performance of TIBET is analyzed and compared with other schemes. Moreover, by studying TCP behavior with an ideal bandwidth estimation, we provide an upper bound to the performance of all possible schemes based on different bandwidth estimates.

Loss differentiation algorithms (LDA) are used to provide TCP with an estimate of the cause of packet losses, to improve performance over heterogeneous networks including wired and wireless links. In this work, we compared by simulation the accuracy of several LDA schemes in various realistic scenarios. We experienced that LDA schemes originally proposed in literature exhibit poor performance in estimating the cause of packet losses. Thus, we propose enhancements to the noncongestion packet loss detection (NCPLD) and Vegas schemes, achieving higher accuracy in all network scenarios. We shown that our proposed enhanced schemes approach reasonably ideal accuracy of LDA having perfect knowledge of the cause of packet losses. These results entail the possibility to adopt such algorithms within TCP congestion control, to achieve higher performance over heterogeneous wireless networks.