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.

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.

Abstract Taking into account the dynamic nature of traffic in telecommunication networks, the robust network design problem is to fix the edge capacities so that all demand vectors belonging to a polytope can be routed. While a common heuristic for this co‐NP‐hard problem is to compute, in polynomial time, an optimal static routing, affine routing can be used to obtain better solutions. It consists in restricting the routing to affinely depend on the demands. We show that a node‐arc formulation is less conservative than an arc‐path formulation. We also provide a cycle‐based formulation that is equivalent to the node‐arc formulation. To further reduce the solution's cost, several new formulations are obtained by relaxing flow conservation constraints and aggregating demands. As might be expected, aggregation allows us to reduce the size of formulations. A more striking result is that aggregation reduces the solution's cost.

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.

Body-to-Body Networks (BBNs) have recently gained momentum as a revolutionary technology for the monitoring of people behavior with real-time updates of medical records and interactive assistance in emergency situations, like the spread of pandemic diseases. This paper investigates the epidemic control issue in mass gathering areas (i.e., the airports) from a practical point of view by using BBNs and by adopting some key features from existing epidemiology models. We first introduce a BBN-based epidemic control framework. Second, we define an Epidemic-aware Routing Metric and then propose a Location-Aided Routing protocol tailored to BBNs, called BB-LAR, along with an epidemic control mechanism, in order to exchange epidemic data and help the authority control unit in detecting and quarantining the infected subjects. Finally, we evaluate the performance of BB-LAR with respect to existing routing schemes in terms of packet delivery ratio, end-to-end delay, and energy consumption.

We state and solve a problem of the optimal geographic caching of content in cellular networks, where linear combinations of contents are stored in the caches of base stations. We consider a general content popularity distribution and a general distribution of the number of stations covering the typical location in the network. We are looking for a policy of content caching maximizing the probability of serving the typical content request from the caches of covering stations. The problem has a special form of monotone sub-modular set function maximization. Using dynamic programming, we find a deterministic policy solving the problem. We also consider two natural greedy caching policies. We evaluate our policies considering two popular stochastic geometric coverage models: the Boolean one and the Signal-to-Interference-and-Noise-Ratio one, assuming Zipf popularity distribution. Our numerical results show that the proposed deterministic policies are in general not worse than some randomized policy considered in the literature and can further improve the total hit probability in the moderately high coverage regime.

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.

This paper focuses on the resource allocation problem in the context of Cloud Computing. More specifically, this work considers the problem of optimizing the mapping cost of Infrastructure as Cloud Service (IaaS) onto a Networked Edge Data-Centers (DCs) with respect to Quality of Service (QoS) requirements. This work proposes to dynamically partition the networked DCs resources over IaaS requests belonging to different QoS classes. In literature, a number of works have proposed IaaS mapping approaches; however their focus was mainly on the cloud hosting requirements and do not take into account the dynamics of IaaS QoS requirements. Consequently, they may not offer QoS guarantees for accepted IaaS requests which may result in a higher customer dissatisfaction ratio. The originality of our work is in the forethought and the investigation of these issues. To do so, a column generation based-formulation is proposed coupled with the Branch and Bound technique in order to solve it efficiently. Doing so, this allows the Cloud Provider to: (i) minimize IaaS mapping cost, and (ii) calculate the optimal and dynamic partitioning of DCs resources to uphold QoS guarantees for IaaS requests.

In this paper, we investigate the spectrum management problem in TV White Space (TVWS) Cognitive Radio Networks using a game theoretical approach, accounting for adjacent-channel interference. TV Bands Devices (TVBDs) compete to access available TV channels and choose idle blocks that optimize some objective function. Specifically, the goal of each TVBD is to minimize the price paid to the Database operator and a cost function that depends on the interference between unlicensed devices. We show that the proposed TVWS management game admits a potential function under general conditions. Accordingly, we use a Best Response algorithm to converge in few iterations to the Nash Equilibrium (NE) points. We evaluate the performance of the proposed game, considering both static and dynamic TVWS scenarios and taking into account users' mobility. Our results show that at the NE, the game provides an interesting tradeoff between efficient TV spectrum use and reduction of interference between TVBDs.

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.

In this paper, we propose a reliable topology design and provisioning approach for Wireless Body Area Networks (named RTDP-WBAN) that takes into account the mobility of the patient while guaranteeing a reliable data delivery required to support healthcare applications' needs. To do so, we first propose a 3D coordinate system able to calculate the coordinates of relay-sensor nodes in different body postures and movements. This system uses a 3D-model of a standard human body and a specific set of node positions with stable communication links, forming a virtual backbone. Next, we investigate the optimal relay nodes positioning jointly with the reliable and cost-effective data routing for different body postures and movements. Therefore, we use an Integer Linear Programming (ILP) model, that is able to find the optimal number and locations of relay nodes and calculate the optimal data routing from sensors and relays towards the sink, minimizing both the network setup cost and the energy consumption. We solve the model in dynamic WBAN (Stand, Sit and Walk) scenarios, and compare its performance to other relaying approaches. Experiment results showed that our realistic and dynamic WBAN design approach significantly improves results obtained in the literature, in terms of reliability, energy-consumption and number of relays deployed on the body.

In recent years, Body-to-Body Networks (BBNs) have gained momentum as a means to monitor people behavior and simplify their interaction with the surrounding environment; thus representing a key element of the Internet of Things (IoT) networking paradigm. Within BBNs, several transmission technologies sharing the same unlicensed band (namely the ISM band) coexist, increasing dramatically the level of interference, which in turn negatively affects the network performance. In this paper, we consider an IoT system composed of several BBNs and we analyze the Cross Technology Interference (CTI) problem caused by the utilization of different transmission technologies that share the same radio spectrum. We formulate an optimization model considering both the Mutual and Cross Technology Interference in order to mitigate the overall level of interference within the IoT system, taking explicitly into account the node mobility. We further develop two heuristic approaches to solve efficiently the interference mitigation problem in large scale network scenarios. Numerical results show that the proposed heuristics represent two efficient and practical alternatives to the optimal solution for solving the CTI mitigation problem in large scale IoT scenarios.

Wireless Body Area Networks (WBANs) represent one of the most promising approaches for improving the quality of life, allowing remote patient monitoring and other healthcare applications. In such networks, traffic routing plays an important role together with the positioning of relay nodes, which collect the information from biosensors and send it towards the sinks. This work investigates the optimal design of wireless body area networks by studying the joint data routing and relay positioning problem in a WBAN, in order to increase the network lifetime. To this end, we propose an integer linear programming model which optimizes the number and location of relays to be deployed and the data routing towards the sinks, minimizing both the network installation cost and the energy consumed by wireless sensors and relays. We solve the proposed model in realistic WBAN scenarios, and discuss the effect of different parameters on the characteristics of the planned networks. Numerical results demonstrate that our model can design energy-efficient and cost-effective wireless body area networks in a very short computing time, thus representing an interesting framework for the WBAN planning problem.

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.

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.