In the realm of the Internet of Vehicles (IoV), the concept of grouping autonomous vehicles into platoons stands out as a promising driving scenario. A platoon comprises interconnected vehicles, with the foremost vehicle designated as the Platoon Leader (PL), while each of those trailing behind is a Platoon Member (PM). In such contexts, information freshness quantified using the Age of Information (AoI) critically ensures road traffic safety. This paper explores the joint packet transmission scheduling and power allocation problem with the objective of minimizing AoI in multi-platoon vehicular networks; these latter exhibiting high dynamics incurring notable uncertainty and complexity. To alleviate this optimization problem’s complexity a decentralized partially observable Markov Decision Process (Dec-POMDP) formulation is adopted. Then, an AoI-aware joint scheduling and power control scheme based on Multi-Agent Twin Delayed Deep Deterministic policy gradient (MATD3) algorithm is proposed. In addition, in order to improve the efficiency of the MATD3’s learning phase, the algorithm has been augmented with Priority Experience Replay (PER). Simulation results show that this approach outperforms the baseline MATD3 method by 17.3% in terms of the achieved mean AoI.

Device-to-device (D2D)-aided mobile edge computing (MEC) has emerged as a key enabling technology for future sixth-generation (6G) wireless networks. The goal of D2D-MEC is to reduce system latency for edge user equipments (UEs) by enabling access to cloud computing capabilities at the network edge, thereby supporting high transmission rates. To address the vulnerability of communication signals to physical obstructions, we employ relay techniques to enhance system performance and extend coverage. However, relay nodes and base station (BS) are typically equipped with large-scale antenna arrays, which lead to significant implementation costs and limiting practical deployment. To address this issue in a cost-efficient manner without sacrificing system performance, movable antenna (MA) technology is introduced. The key idea of MA technology lies in dynamically optimizing antenna positions to improve system capacity. Therefore, we propose a novel resource allocation framework for an movable relay-aided D2D-MEC system. The proposed scheme jointly optimizes the MA positions at UEs, relays, and the BS, along with the associated beamforming vectors, MEC server resource allocation, and computational task offloading rates. The objective is to minimize the maximum system latency while satisfying both computation and communication rate constraints. Furthermore, considering that current MA control mechanisms primarily rely on mechanical actuation, MA movement delay is incorporated into the latency model to capture the trade-off between antenna mobility and system delay. The resulting optimization problem is non-convex and involves multiple coupled variables. To solve this problem, we develop a parallel and distributed algorithm based on the penalty dual decomposition (PDD) framework, which is further integrated with the successive convex approximation (SCA) method to obtain a suboptimal solution. Simulation results demonstrate that the proposed algorithm significantly reduces system latency and enhances overall efficiency compared to benchmark schemes employing conventional fixed-position antennas (FPAs) at the relays and BS.

In the realm of complex IoT-based smart city advancements, the real-time reception, processing, and maintenance of up-to-date multi-sourced data is essential for ensuring efficient urban infrastructure operations and functionality. Beyond the typical Age of Information (AoI), such applications vociferate the urgent need for a new metric, capable of capturing and accounting for the age of the aggregated data; namely, the Age of Aggregated Information (AoAI). This paper addresses an AoAI minimization problem for mixed-paths IoT networks. This problem is formulated as a Mixed Integer Linear Program (MILP) that jointly considers data packet scheduling and routing as well as nodal energy and power constraints. To overcome this problem’s notable complexity, the Column Generation Algorithm (CGA) is used to break it down into a Relaxation Master Problem (RMP) and a Pricing Problem (PP) with the objective of identifying optimal scheduling and aggregation strategies. Experimental results demonstrate the potency of the proposed CGA-based algorithm in generating accurate sub-optimal solutions with no more than 1.06% deviation from their optimal counterparts; an outstanding result that existing algorithms have failed to achieve. The variations in AoAI and latency trends were compared and found to be in-line for fixed network configurations.

This work presents an investigation and assessment framework, which, supported by realistic data, aims at provisioning operators with in-depth insights into the consumer-perceived Quality-of-Experience (QoE) at public Electric Vehicle (EV) charging infrastructures. Motivated by the unprecedented EV market growth, it is suspected that the existing charging infrastructure will soon be no longer capable of sustaining the rapidly growing charging demands; let alone that the currently adopted ad hoc infrastructure expansion strategies seem to be far from contributing any quality service sustainability solutions that tangibly reduce (ultimately mitigate) the severity of this problem. Without suitable QoE metrics, operators, today, face remarkable difficulty in assessing the performance of EV Charging Stations (EVCSs) in this regard. This paper aims at filling this gap through the formulation of novel and original critical QoE performance metrics that provide operators with visibility into the per-EVCS operational dynamics and allow for the optimization of these stations’ respective utilization. Such metrics shall then be used as inputs to a Machine Learning model finely tailored and trained using recent real-world data sets for the purpose of forecasting future long-term EVCS loads. This will, in turn, allow for making informed optimal EV charging infrastructure expansions that will be capable of reliably coping with the rising EV charging demands and maintaining acceptable QoE levels. The model’s accuracy has been tested and extensive simulations are conducted to evaluate the achieved performance in terms of the above-listed metrics and show the suitability of the recommended infrastructure expansions.

Data center networks have been gaining a lot of attention in recent years. These networks are scaling up quickly with the explosive nature of current applications. Lately, a lot of efforts have been exerted to improve the performance of these networks compared to the often performance-lagging standard Clos-based topologies. One of the approaches for performance improvement is to use alternative data center network topologies. Consequently, researchers explored topologies based on Expander Graphs (EGs), such as Jellyfish, Xpander, and STRAT, where they exploited the sparse and incremental nature of these new topologies. This paper investigates the STructured Re-Arranged Topology (STRAT) as a potentially robust and efficient design for next-generation data centers. Robustness and throughput metrics are adopted to benchmark the performance of STRAT against the well-known Expander architectures, which show better performance than that of present topologies, (e.g., Fat-Tree, BCube). This paper shows that STRAT has better structural properties than Jellyfish and Xpander, making it more robust to switch and link failures. Specifically, STRAT possesses lower average shortest path length and diameter, higher spectral gap, and higher algebraic connectivity. These exceptional properties allow STRAT to achieve better throughput. Such observations are validated through extensive flow and packet level simulations, demonstrating STRAT’s superior performance in terms of the flow completion time as compared to other Expanders.

This work targets enhancing the quality of charging experience in Electric Vehicle (EV) Public Charging Infrastructure (PCI) networks. The estimation uncertainty of waiting times at charging stations (CSs) hinders the proliferation of such networks and, hence, decelerates EV adoption. Currently, most EV owners prefer to use private chargers; thus, overloading the energy distribution network leaving PCIs under-utilized. Consequently, it becomes important for PCI operators to provide customers with accurate waiting time estimates at various CSs; therefore, allowing them to make more informed CS selections. The per-CS EV waiting times reveal possible CS overloads, which, when frequently repetitive, indicate the need for PCI up-scaling to satisfy increasing demands; hence, ensuring elevated customer QoE. This paper leverages recent real-world data to unveil the statistical properties of EV charging times that, unlike existing studies, are found to be best captured by an Erlang-${k}$distribution. Also, the per-CS charging request arrival processes are characterized under various scheduling policies. It is established hereafter that CSs can be accurately modelled as single-server queuing systems. Finally, extensive simulations are conducted to verify the accuracy of the proposed models and provide further insights into the waiting time performance achieved by each of the adopted scheduling policies.

In the context of Software Defined Networking (SDN) scenarios, the deployment of multi-path routing has been trending as one of the practical approaches. It serves the two-fold objective of improving the reliability of Service Function Chains (SFCs) and reducing end-to-end delays through parallel processing; this latter being this paper’s focal point given it is one of the fundamental objectives of 6G. The literature encloses numerous publications revolving around the exploitation of Virtual Network Function (VNF) duplication and optimal placement to enable parallel processing. However, very little attention has been allocated to segmented VNFs with parallel multi-path data traffic flow routing to catalyze service completion. In reality, the application of segmented task processing is now widely used in our Internet life (e,g, real-time video on Youtube). In order to realize the ultra-low end-to-end delay of SFC, we introduce the segmented VNF processing window and implement VNF processing tasks in batches/windows with multi-path routing. Herein, a novel Parallel Multi-Path service flow Routing with processing Windows (PMPRW) scheme is proposed. The PMPRW is formulated as a Mixed Integer Linear Program (MILP), owing to the complexity of which, a Column-Generation (CG) based framework is developed to generate accurate sub-optimal solutions that achieve the same performance as the optimal solution. In order to accelerate the process and enhance the performance, we propose an extended Column Fixing (CF) strategy to help generate new columns in CG. Extensive simulations are conducted to gauge the merit of PMPRW and demonstrate its superiority (as opposed to single-path routing). PMPRW achieves desirable performance by concurrently reducing the overall end-to-end delay (e.g., 22% through parallel dual-path routing).

Wireless Sensor Networks (WSNs) require timely information updates for safety and operational efficiency. The Age of Information (AoI) quantifies the freshness of information exchanged among WSN nodes. However, in the presence of noise and fading, sensors communicating with each other as well as with Access Points (APs) may suffer from degraded communication reliability. Here, Reconfigurable Intelligent Surfaces (RISs) are capable of enhancing communication links and, thus, maintaining information freshness. This paper aims at minimizing the AoI perceived by communicating sensors in multi-RIS-assisted WSNs. Precisely, an original AoI-minimal, Deadline-constrained and Cooperative Multi-RIS-assisted (ADCMR) wireless sensor communication framework is presented. This framework embeds a convoluted Integer Linear Program (ILP) formulation aiming at optimizing the schedules of collaborative data transfers subject to deadline and RIS selection constraints. Owing to this ILP’s remarkable complexity, a GReedy Algorithm (GRA) is proposed to generate acceptable sub-optimal schedules with a maximum of 6.66% performance difference compared to their optimal counterparts. GRA, though, is not scalable. This problem is resolved using a Lagrange Relaxation Algorithm (LRA). The difference between LRA and the optimal solution is 2.2%. Also, for large-scale networks, LRA outperforms GRA by 24.8%. Extensive simulations and numerical analyses are conducted to gauge LRA’s benefits and highlight its notable AoI performance improvements over existing benchmarks.

In line with the global push towards smart cities, the world is increasingly adopting Electric Vehicles (EVs). This increased EV proliferation is putting the Public Charging Infrastructure (PCI) under a large strain. To this end, this work presents a data-driven analysis of the Quality of Service (QoS) on the current EV PCI. This work presents a comprehensive set of metrics that are developed to evaluate the QoS at the current PCI in Quebec, Canada. The analysis is performed on a real dataset covering 5 full years of over 7,000 EV Charging Stations (EVCSs) in Quebec. This data is then used to create a forecast model for predicting future EV charging requests and assessing their impact on the QOS at the current PCI deployment levels. The developed metrics and forecast model are used to recommend new EVCS deployment sites to guarantee acceptable QoS levels in the future based on the current trends in EV adoption.

Cooperative-Autonomous-Driving (CAD) systems stringently require that vehicle status information (e.g, speed, position, etc) be timely disseminated for safety reasons. CAD systems rely on real-time information to make critical decisions; hence, the paramount criticality of temporally valid information generation and dissemination. However, the timely information update messages’ delivery faces numerous challenges due to the highly alternating wireless signal propagation in vehicular environments as a result of, for instance, shadowing and blockage, which lead to the unavailability of reliable communication links between cooperating vehicles. Under such harsh conditions, Reconfigurable-Intelligent-Surfaces (RISs) have been proven to highly contribute in mitigating the propagation-induced impairments of the wireless environments and, hence, promoting more robust communication links, which, in turn, allow for maintaining the required freshness of information. In the above-context, this paper revolves around the minimization of the Age-of-Information (AoI) perceived by each of a CAD system’s destination node. The problem is formulated as an Integer-Linear-Program (ILP), which turns out to be quite complex. To work around this complexity, it is proposed herein to use decomposition based on the Lagrangian relaxation method, which largely facilitates the problem’s resolution following typical dynamic programming methodologies. Consequently a feasible solution is extracted using a relatively simple heuristic. An analytical framework is established to reveal insights into the proposed solution and gauge its merits through the establishment of a thorough simulation framework involving various scenarios aiming at verifying its correctness, validity and superiority as compared to other solutions derived using the state-of-the-art branch-and-cut method implemented by CPLEX.

The work presented in this article aims at improving the ground vehicle connectivity in the context of an intermittent vehicle-to-UAV (V2U) communication scenario where vehicles opportunistically establish time-limited connectivity with passing by unmanned aerial vehicles (UAVs) serving as flying base stations responsible for routing incoming vehicle data over backbone networks and/or the Internet. As opposed to existing work in the literature where vehicles are only allowed to establish direct connectivity with in-range UAVs, this work aims at also exploiting the possible formation of vehicular clusters and, hence, the feasibility of intervehicular communications to establish multihop paths connecting source vehicles to destination UAVs. A mathematical model is presented for the purpose of capturing the nodal (i.e., vehicles and UAVs) mobility dynamics and derive an expression for the overall V2U connectivity probability as well as the overall average vehicle connection time. Extensive simulations are conducted in order to adduce the validity and accuracy of the proposed model and provide further insights into the connectivity sensibility to fundamental system parameters.

The Vehicle-as-a-Resource is an emerging concept that allows the exploitation of the vehicles' computational resources for the purpose of executing tasks offloaded by passengers, vehicles, or even an Internet-of-Things devices. This article revolves around a scenario where a roadside unit located at the edge of a hierarchical multitier edge computing subnetwork resorts to the utilization of idle vehicles computational resources through a fog-enabled substructure yielding a cost-effective computational task offloading solution. In this context, scheduling the offload of these tasks to the appropriate vehicles is a challenging problem that is subject to the interaction of major role-playing parameters. Among these parameters are the variability of vehicles availability and their computational power, the individual tasks' weighted priorities and their deadlines, the tasks required computational power as well as the required data to upload/download. This article proposes an infrastructure-assisted task scheduling scheme where the roadside unit receives computational tasks from different sources and schedules these tasks over a computationally capable vehicle residing within the roadside unit's range. The aim is to maximize the weighted number of admitted tasks while considering the constraints mentioned above. Compared to other works, this article broaches a more realistic scenario by considering a more accurate computational task and system model. Our system considers both the latency and throughput of task accomplishments by maximizing the weighted number of admitted tasks while at the same time respecting the tasks accompanied deadlines. Both radio and computational resources are part of the optimization problem. After proving the NP-hardness of the scheduling problem, we formulated the problem as a mixed-integer linear program. A Dantzig-Wolfe decomposition algorithm is proposed which yields to a master program solvable by the Barrier algorithm and subproblems solved optimally with a polynomial-time dynamic programming approach. Thorough numerical analysis and simulations are conducted in order to verify and assert the validity, correctness, and effectiveness of our approach compared to branch and bound and greedy algorithms.

Network Function Virtualization (NFV) converts network functions executed by costly middleboxes into instances of Virtual Network Functions (VNFs) hosted by industry-standard Physical Machines (PMs). This has proven to be quite an efficient approach when it comes to enabling automated network operations and the elastic provisioning of resources to support heterogeneous services. Today's revolutionary services impose a remarkably elevated reliability together with ultra-low latency requirements. Therefore, in addition to having highly reliable VNFs, these VNFs have to be optimally placed in such a way to rapidly route traffic among them with the least utilization of bandwidth. Hence, the proper selection of PMs to meet the above-mentioned reliability and delay requirements becomes a remarkably challenging problem. None of the existing publications addressing such a problem concurrently adopts VNF decomposition to enhance the flexibility of the VNFs' placement and a hybrid routing scheme to achieve an optimal trade-off between the above-mentioned objectives. In this paper, a VNF-decomposition-based backup strategy is proposed together with a delay-aware hybrid multipath routing scheme for enhancing the reliability of NFV-enabled network services while jointly reducing delays these services experience. The problem is formulated as a Mixed Integer Linear Program (MILP) whose resolution yields an optimal VNF placement and traffic routing policy. Next, the delay-aware hybrid shortest path-based heuristic algorithm is proposed to work around the MILP's complexity. Thorough numerical analysis and simulations are conducted to validate the proposed algorithm and evaluate its performance. Results show that the proposed algorithm outperforms its existing counterparts by 7.53% in terms of computing resource consumption.

This paper investigates the data-delivery latency in the context of intermittent vehicle-to-UAV (V2U) communications. Precisely, a V2U communication scenario is considered where vehicles opportunistically establish connectivity with passing by UAVs for a limited period of time during which these vehicles transmit data packets to in-range UAVs serving as flying base stations and, in turn, are responsible for delivering these packets to backbone networks and/or routing them over the Internet. A mathematical framework is established with the objective of modeling the vehicles' OnBoard Units' (OBUs') buffers as single-server queueing systems. The established queueing model will allow for the evaluation of the V2U communication system in terms of the average data packet delivery delay. Extensive simulations are conducted with the objective of asserting the validity and accuracy of the proposed queueing model as well as providing further insights into the delay sensibility to various system parameters.

Connection and Service Request (CSR) scheduling affects the performance of a Mobile Relay Node (MRN) in relay-assisted LTE network in terms of crucial Quality-of-Service (QoS) metrics such as the blocking probability and the system's response time. In this paper, a novel Deadline-constrained Connection and service Request Scheduling (DCRS) scheme is proposed with the objective of improving an MRN's performance in terms of the above-mentioned metrics. A stochastic queueing model is formulated for the purpose of capturing the MRN's behavioral dynamics and evaluating its performance as it operates under DCRS. Simulations are conducted in order to verify the model's results' validity and accuracy. Moreover, the MRN performance achieved under DCRS is compared to that achieved under the typically adopted First-Come-First-Servce (FCFS) scheme. Results indicate that DCRS remarkably outperforms FCFS.

Driven by the expeditious evolution of the Internet of Things, the conventional vehicular ad hoc networks will progress toward the Internet of Vehicles (IoV). With the rapid development of computation and communication technologies, IoV promises huge commercial interest and research value, thereby attracting a large number of companies and researchers. In an effort to satisfy the driver's well-being and demand for continuous connectivity in the IoV era, this paper addresses both safety and quality-of-service (QoS) concerns in a green, balanced, connected, and efficient vehicular network. Using the recent advances in training deep neural networks, we exploit the deep reinforcement learning model, namely deep Q-network, which learns a scheduling policy from high-dimensional inputs corresponding to the current characteristics of the underlying model. The realized policy serves to extend the lifetime of the battery-powered vehicular network while promoting a safe environment that meets acceptable QoS levels. Our presented deep reinforcement learning model is found to outperform several scheduling benchmarks in terms of completed request percentage (10-25%), mean request delay (10-15%), and total network lifetime (5-65%).

Establishing a continuous multi-hop data communication path among an arbitrary pair of vehicular nodes is severely affected by the natural vehicular traffic's restrictive mobility dynamics (e.g. flow rate, speed, direction of movement, etc). This paper proposes a novel Vehicular Traffic Control (VTC) scheme that has the objective of regulating the vehicles' speeds in a way to get them steadily navigating in the proximity of each other at inter-vehicular distances that do not exceed their respective OnBoard Units' ranges. This triggers the formation of robust and long-lived communication links connecting the vehicles together; hence, increasing the path availability probability. A mathematical framework is established to evaluate the performance of VTC in terms of crucial Quality-of-Service (QoS) metrics such as the path availability and average end-to-end packet delivery delay. The validity and accuracy of the proposed model is verified through simulations. The reported results constitute a tangible proof of the benefits/merit of VTC in the context of a vehicular networking scenario characterized by low-to-medium flow rates.

Network Function Virtualization (NFV) has revolutionized service provisioning in cloud datacenter networks. It enables the complete decoupling of Network Functions (NFs) from the physical hardware middle boxes that network operators deploy for implementing service-specific and strictly ordered NF chains. Precisely, NFV allows for dispatching NFs as instances of plain software called virtual network functions (VNFs) running on virtual machines hosted by one or more industry standard physical machines. Nevertheless, NF softwarization introduces processing vulnerability (e.g., failures caused by hardware or software, and so on). Since any failure of VNFs could break down an entire service chain, thus interrupting the service, the functionality of an NFV-enabled network will require a higher reliability compared with traditional networks. This paper encloses an in-depth investigation of a reliability-aware joint VNF chain placement and flow routing optimization. In order to guarantee the required reliability, an incremental approach is proposed to determine the number of required VNF backups. Through illustration, it is shown herein that the formulated single path routing model can be easily extended to support resource sharing between adjacent backup VNF instances. This paper advocates the absolute existence of a share-resource-based VNF assignment strategy that is capable of trading off all of the reliability, bandwidth, and computing resources consumption of a given service chain. A heuristic is proposed to work around the complexity of the presently formulated integer linear programming (ILP). Thorough numerical analysis and simulations are conducted in order to verify and assert the validity, correctness, and effectiveness of this proposed heuristic reflecting its ability to achieve very close results to those obtained through the resolution of the complex ILP within a negligible amount of time. Above and beyond, the proposed resource-sharing-based VNF placement scheme outperforms existing resource-sharing agnostic schemes by 15. 6% and 14.7% in terms of bandwidth and CPU utilization respectively.

User request (UR) service scheduling is a process that significantly impacts the performance of a cloud data center. This is especially true since essential quality-of-service (QoS) performance metrics such as the UR blocking probability as well as the data center's response time are tightly coupled to such a process. This paper revolves around the proposal of a novel Deadline-Aware UR Scheduling Scheme (DASS) that has the objective of improving the data center's QoS performance in term of the above-mentioned metrics. A minority of existing work in the literature targets the formulation of mathematical models for the purpose of characterizing a cloud data center's performance. As a contribution to covering this gap, this paper presents an analytical model, which is developed for the purpose of capturing the system's dynamics and evaluating its performance when operating under DASS. The model's results' accuracy are verified through simulations. Also, the performance of the data center achieved under DASS is compared to its counterpart achieved under the more generic First-In-First-Out (FIFO) scheme. The reported results indicate that DASS outperforms FIFO by 11 to 58 percent in terms of the blocking probability and by 82 to 89 percent in terms of the system's response time.

The proper design of a vehicular network is the key expeditor for establishing an efficient Intelligent Transportation System, which enables diverse applications associated with traffic safety, traffic efficiency, and the entertainment of commuting passengers. In this paper, we address both safety and Quality-of-Service (QoS) concerns in a green Vehicle-to-Infrastructure communication scenario. Using the recent advances in training deep neural networks, we present a deep reinforcement learning model, namely deep Q-network, that learns an energy-efficient scheduling policy from high-dimensional inputs corresponding to the characteristics and requirements of vehicles residing within a RoadSide Unit's (RSU) communication range. The realized policy serves to extend the lifetime of the battery-powered RSU while promoting a safe environment that meets acceptable QoS levels. Our presented deep reinforcement learning model is found to outperform both random and greedy scheduling benchmarks.

Real-time interactive application workloads (e.g., Web search, social networking, and so on) appear in the form of a large number of mini requests and responses flowing over the datacenters' networks. They end up being sewed all together to constitute a user-requested task or computation (e.g., display a complete Facebook timeline). Applications as such strictly impose low latency flow completion, since the service's quality is decreed by quick aggregation of responses to the largest possible fraction of requests and their delivery back to the user. This paper presents a deadline-aware flow scheduling (DAFS). In addition to reducing the average flow completion time (FCT), DAFS aims at decreasing the deadline mismatch and blocking probabilities, hence improving the average application throughput. An analytical queuing model is formulated herein to capture the datacenter's network dynamics and evaluate its performance when operating under DAFS. The model is validated through extensive simulations whose results also show that DAFS outperforms existing multi-queue-based priority mechanisms by 52% in terms of the average FCT and a range of 7%-29% in terms of the average throughput.

Traditionally, service-specific network functions (NFs) (e.g., Firewall, intrusion detection system, etc.) are executed by installation-and maintenance-costly hardware middleboxes that are deployed within a datacenter network following a strictly ordered chain. NF virtualization (NFV) virtualizes these NFs and transforms them into instances of plain software referred to as virtual NFs (VNFs) and executed by virtual machines, which, in turn, are hosted over one or multiple industry-standard physical machines. The failure (e.g., hardware or software) of any one of a service chain's VNFs leads to breaking down the entire chain and causing significant data losses, delays, and resource wastage. This paper establishes a reliability-aware and delay-constrained (READ) routing optimization framework for NFV-enabled datacenter networks. READ encloses the formulation of a complex mixed integer linear program (MILP) whose resolution yields an optimal network service VNF placement and traffic routing policy that jointly maximizes the achieved respective reliabilities of supported network services and minimizes these services' respective end-to-end delays. A heuristic algorithm dubbed Greedy-k-shortest paths (GSP) is proposed for the purpose of overcoming the MILP's complexity and develop an efficient routing scheme whose results are comparable to those of READ's optimal counterparts. Thorough numerical analyses are conducted to evaluate the network's performance under GSP, and hence, gauge its merit; particularly, when compared to existing schemes, GSP exhibits an improvement of 18.5% in terms of the average end-to-end delay as well as 7.4% to 14.8% in terms of reliability.

Network Function Visualization (NFV) enables the complete decoupling of Network Functions (NFs) (e.g., firewall, intrusion detection, routing, etc.) from physical middleboxes used to implement service-specific and strictly ordered chains of these NFs. Precisely, NFV allows for dispatching NFs as plain software instances called Virtual Network Functions (VNFs) running on virtual machines hosted by one or more industry standard physical machines. This, however, introduces vulnerabilities (e.g., hard-/soft-ware failures, etc) causing the break down of the entire VNF chain. The functionality of NFV-enabled networks impose higher reliability requirements than traditional networks. This paper encloses an in-depth investigation of a reliability-aware joint VNF placement and flow routing optimization problem. This problem is formulated as a complex Integer Linear Program (ILP). A heuristic is proposed in order to overcome this ILP's complexity. Thorough numerical analysis are conducted to verify and assert the correctness and effectiveness of the proposed heuristic.

Vehicular Ad hoc Networks have been receiving significant interest during the past years as they support both safety and non-safety applications for passengers commuting onboard smart vehicles. Vehicles may communicate with each others for the purpose of sharing information. Moreover, they may be privileged by Broadband Internet access as well as other services provisioned by stationary Roadside Units (RSUs) deployed along the roadways. When a vehicle leaves the coverage range of an RSU, it enters a dark area. However, it may still maintain connectivity with the RSU through multi-hop communication with other cooperative vehicles serving as intermediate relays. In this paper, we study the probability of establishing a connectivity path between a far away vehicle residing in a dark area and an RSU deployed along a roadway experiencing free-flow traffic conditions. For this purpose, we establish a stochastic mathematical framework which jointly considers the availability of intermediate relay vehicles as well as their ability to capture the communication channel in a contention-based MAC environment. Extensive simulations were conducted for the purpose of validating the derived expressions and examining the throughput performance of the system.

This paper revolves around the evaluation of the achievable throughput in the context of an IEEE 802.11p-based vehicular subnetwork scenario where a completely isolated source vehicle, S, desires to communicate with a distant stationary Roadside Internet Gateway (RIG), D. Multi-hop inter-vehicular communication is exploited for the purpose of establishing a path between S and D along which downstream cooperative vehicles serve intermediate packet relays. The formation of such a path is governed by the vehicular traffic behaviour as well as the per-hop contention-oriented data forwarding process. Following the formation of a continuous chain of in-range cooperative vehicles between an arbitrary source-destination pair (S,D), a stochastic analytical framework is developed with the objective of determining the probability of successful data transfer from S to D taking into account the per-hop vehicle contentions for channel access. Then, theoretical expressions for the achievable per-hop as well as the end-to-end throughput are presented. Simulations are conducted for purpose of validating the presented analysis and evaluating the considered subnetwork's performance.

Broadband Internet access provisioning in vehicular environments requires establishing on-the-fly connectivity between mobile vehicles and stationary Internet gateways deployed along roadways. The literature encloses various works revolving around vehicle-to-infrastructure (V2I) communication schemes designed to cater for this objective. In this paper, two novel complexity minimal MAC schemes are proposed for drive-thru Internet (DTI) access provisioning systems. The first of these schemes is called the random vehicle selection (RVS) scheme, and the second is called the least residual residence time (LRT) scheme. A mathematical framework is established with the objective of modeling a vehicle's onboard unit's buffer and evaluating its performance under RVS and LRT, in terms of several quality-of-service metrics. Extensive simulations are conducted for the purpose of verifying the proposed models' validity and accuracy.

This paper presents a consice yet comprehensive description of a DSA-based Vehicle-to-Infrastructure (V2I) communication system operating under spectral scarcity conditions. Existing mathematical models for such a system overlook some but essential ones of its behavioural characteristics. Thus, these models' reported performance results seem to be unrealistically overoptimistic. In this paper, a simulation study is conducted in the context of a real-life scenario. As opposed to the existing studies, the study herein aims at providing more insights into the dynamics of this type of communication systems and assessing its performance in terms of several new metrics.

This paper presents a concise yet comprehensive description of a multichannel vehicle-to-infrastructure communication system. Existing mathematical models for such a system overlook some of its essential behavioral characteristics such as the reneging, force termination, and, ultimately, blocking of service requests (SRs). Thus, the reported performance results obtained from these models seem to be unrealistically overoptimistic. Accordingly, in this paper, a multiserver queueing model is proposed for the purpose of accurately capturing the dynamics of the aforementioned communication system and evaluating its performance. The proposed model is renowned for its complexity and the nonexistence of closed-form analytical expressions that characterize its fundamental performance metrics. Hence, approximations were exploited as a means to enhance this model's mathematical tractability. Simulations are conducted in the context of a realistic scenario with the objective of validating the proposed approximate model, verifying its accuracy, and characterizing the system's performance in terms of several new metrics. The simulations' results indicate a cataclysmic SR blocking probability in the range of 65%-85%.

The open literature encloses numerous studies on the efficiency of retransmission mechanisms used in typical data communication networks for the purpose of recovering from packet transmission errors or losses. This paper revolves around the design and analysis of a Delay-Aware Data Delivery (DADD) scheme for Vehicular Intermittently Connected Networks (VICNs). At the heart of DADD is a novel mechanism that allows a source stationary roadside unit (SRU) to carry out necessary bundle retransmissions to high speed vehicles newly entering its communication range. In turn, these vehicles will guarantee delay-minimal delivery of the retransmitted bundles to the destination SRU. A mathematical model is developed to characterize the operation of the source SRU under DADD as well as to evaluate the resulting bundle delivery delay. To verify the validity and the accuracy of the proposed model, extensive simulations are conducted where the performance of DADD is compared to that of two other existing schemes. Results show that DADD outperforms these two schemes by 14.28% to 36.84%.

This paper proposes a novel bundle delivery scheme (BDS) aimed at achieving a delay-minimal bundle delivery in the context of an intermittent roadside network. The realization of this objective is challenging whenever network information is completely unavailable. The concept of virtual space (VS) presents itself as an efficient solution that allows the source to perform necessary data bundle retransmissions to a subset of arriving vehicles. In turn, these vehicles will secure earlier delivery of the retransmitted bundles to the destination. A thorough empirical performance evaluation of the BDS shows that this scheme exhibits a delay improvement of 22.6%-40% relative to other existing schemes.

This paper presents an in-depth investigation on the feasibility of dynamic spectrum access (DSA) in vehicular environments. We present a comprehensive description of the DSA-based vehicle-to-infrastructure (V2I) communication as it takes place in the context of a scenario where spectral resources are limited. Founded on top of this description is a queueing model whose primary objectives are to capture and characterize the dynamics of this type of communication system and assess its performance in terms of several classical metrics. Simplicity and tractability distinguish the proposed model herein from existing models in the literature. Extensive simulations and numerical analysis are conducted for the purpose of validating the proposed model, evaluating the performance of DSA-based communication, and highlighting its limitations.

This paper proposes a novel Delay Optimal Data Delivery (DODD) scheme that aims at achieving a delay-minimal bundle delivery in the context of an intermittently connected vehicular network. The realization of this objective is challenging since network information is completely unavailable. The concept of Virtual Space presents an efficient solution that allows the source to perform necessary data bundle retransmissions to a subset of arriving vehicles. In turn, these vehicles will secure earlier delivery of the retransmitted bundles to the destination. The mathematical performance evaluation of DODD as well as extensive simulations show that this scheme exhibits a delay improvement of 22:6% to 40% relatively to other existing schemes.

Delay-optimal data delivery in Vehicular Intermittently Connected Networks (VICNs) is challenging since vehicular traffic is affected by numerous recurring and completely random events. Some of these events cause breakdowns and jams while others subserve traffic stability. Researchers observed that mobile vehicles might be wisely exploited to connect two isolated, Stationary Roadside Units (SRUs). In this context, the design of effective delay-minimal data relaying strategies is receiving significant attention. However, many existing such schemes either do not adequately model vehicular traffic behaviours or adapt typical Internet packet-like forwarding protocols to VICNs. In contrast, this manuscript presents a concise, yet comprehensive study of vehicular traffic states based on which a "comme-il-faut" vehicular traffic model is established. This model captures the fundamental traffic characteristics and enables the selection of appropriate distributions for vehicular flow and speeds that parallel the realistic measurements made by traffic theorists. These distributions constitute the basis of a novel Probabilistic Bundle Release Scheme with Bulk Bundle Release (PBRS-BBR) that is proposed with the objective to minimize the average bundle delivery delay. An analytical queueing model is formulated to assess the performance of PBRS-BBR under medium-to-light vehicular traffic. Extensive simulations are conducted to prove the model's validity and accuracy.

The performance of vehicular data networks (VDNs) is highly dependent on vehicular traffic. Existing studies on VDNs consider custom-developed traffic models that mimic real-life vehicular traffic behavior and prepare the ground for accurate VDN performance evaluation. Traffic evolution is affected by numerous random events. Some developed models are microscopic. They independently consider some possible factors (e.g., weather, road geometry, and drivers' skills). These microscopic models are complex, and their implementations may be costly. Other models are macroscopic. They revolve around only the following three major traffic parameters: 1) density; 2) flow; and 3) speed. The majority of such existing models are unrealistic, because they are based on restrictive assumptions tailored to their enclosing study. Comparing the performance of VDN protocols becomes adequate if and only if these protocols are all developed on top of the same traffic model. Unfortunately, the opposite is true. Hence, the design of a generic traffic model that serves as a basis for future studies on VDNs is equally urgent and important. This paper presents a comprehensive and traffic-theory-inspired macroscopic description of vehicular traffic behavior over roadway facilities that operate under free-flow traffic conditions. Accordingly, a simple and tractable macroscopic traffic model is proposed. Extensive simulations are conducted to verify the validity of the proposed model and its high accuracy.

The use of vehicular infrastructure to establish connectivity between isolated stationary Information Relay Stations is an appealing application of Terrestrial Delay-Tolerant Networking. A source opportunistically releases data bundles to vehicles that randomly enter its range. In turn, those vehicles store the received bundles, carry and deliver them to their intended destination. It follows that a contemporaneous source- destination end-to-end path does not exist. Consequently, bundles experience longer queueing periods at the source. Under such circumstances, the end-to-end bundle delivery delays become several orders of magnitude higher than those experienced in traditional wireless networks. In this context, bundle delivery delay minimization emerges as a challenging problem that has not been adequately addressed in the open literature. This paper proposes a simple Probabilistic Bundle Relay Strategy with Bulk Bundle Release (PBRS-BBR) that aims at minimizing the average end-to-end bundle delivery delay while capturing the essence of vehicular delay-tolerant networking in that it revolves around minimal network information knowledge. A queueing model is formulated to represent stationary sources operating under PBRS-BBR. This model is mathematically analyzed and validated through extensive simulations that gauge its merit.

A persisting major challenge in Vehicular Delay- Tolerant Networks (VDTNs) is the delay minimization of data delivery when communicating nodes are stationary, arbitrarily deployed along roadsides and considerably apart that they cannot establish direct communication between each other. A source opportunistically releases bundles of data to cooperating vehicles passing by, hoping that they will successfully deliver them to the intended destination. Several complex strategies that tackle this problem have been proposed in the open literature. Nevertheless, these strategies often implicitly assume complete network knowledge. In this paper, we propose a rather simple Probabilistic Bundle Relay Strategy (PBRS) that relaxes the availability of complete network information. A queuing model is formulated to represent VDTN stationary sources where PBRS is deployed. We introduce the bundle release probability parameter which expresses the likelihood that a bundle is released by the source to a vehicle passing by. The proposed model is studied analytically and theoretical expressions of its characteristic parameters are all derived. In particular, we compute the time it takes to release a head-of-line bundle (referred to as the bundle's service time). Moreover, the model is validated through a simulation study that gauges its merit. The simulation results show that even with partial network knowledge the proposed queueing model can guarantee acceptable bundle delivery delay.