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.

The evolution of 6G networks introduces heterogeneous services with stringent computing and latency demands. However, constrained edge resources, intricate task dependencies, and dynamic network fluctuations intensify resource contention, increasing the risk of node faults and service interruption. Current fault-tolerant methodologies lack the necessary adaptability to handle the coupled complexity of task interdependencies and volatile resource states, leading to sub-optimal decisions or excessive system overhead. To address these challenges, this paper innovatively proposes TransDiffuse—an intelligent preemptive service migration framework for 6G edge networks. First, the framework employs a Transformer-GAT hybrid model to capture long-range temporal load dynamics and spatial topological constraints, enabling accurate failure prediction. Second, to navigate the trade-off between migration overhead and service robustness, we devise a diffusion-based decision module. This module efficiently explores the discrete combinatorial solution space to synthesize near-optimal service orchestration. Furthermore, a comprehensive evaluation system is constructed to validate the effectiveness of TransDiffuse. Experiments demonstrate that TransDiffuse reduces energy consumption by 32.4%, decreases task completion time by 25.6%, and improves resource balance by 18.7%, while keeping service violations below 5%. This work achieves joint optimization of energy, delay, and resource efficiency, offering a robust solution for resilient service orchestration in 6G edge networks.

Virtual reality (VR) demands extensive computation while imposing strict requirements for ultra-low latency, placing a significant burden on wireless communication systems. In recent years, there has been a growing interest in leveraging unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) as a promising technology to provide flexible computing resources at the edge of wireless networks. To meet the computational demands of VR, we propose a collaborative three-layer edge computing framework assisted by multiple UAVs. This framework enables VR rendering tasks to be executed locally on user devices or offloaded to UAVs and base station (BS) for execution. By jointly optimizing the flight trajectories of UAVs and the rendering modes of users, we aim to maximize the average rendering completion rate, defined as the ratio of successfully completed VR rendering tasks within the specified delay constraints, while minimizing the average energy consumption of UAVs. To enhance adaptability, we adopt a multi-agent twin delayed deep deterministic policy gradient (MATD3) approach that provides an efficient strategy for multi-UAV-assisted VR rendering, even in partially observable scenarios. Simulation results validate our proposed approach and demonstrate that the MATD3 algorithm surpasses the classical multi-agent deep deterministic policy gradient (MADDPG) algorithm in terms of convergence speed and the average rendering completion rate.

Multiaccess edge computing (MEC) plays a crucial role in providing low-latency and high-data transmission services to Internet of Things (IoT) devices. However, in remote areas where deploying edge devices is challenging, optimizing delay remains a significant research focus. To address this issue, our research investigates a multidrones-assisted IoT task offloading model. In this model, tasks generated by IoT devices equipped with energy harvesting (EH) capabilities are offloaded to MEC servers with the assistance of multiple drones. In order to monitor and manage the energy consumption of IoT devices and the task backlog of edge servers, the energy consumption and task update queues are established. We formulate a mixed integer nonlinear programming (MINLP) problem, which aims to optimize the allocation of communication and computation resources to minimize the execution latency of IoT devices. To ensure the stability of each queue, we employ the weighted perturbation method within the Lyapunov optimization framework to decompose the original problem. And a low complexity multidrones assisted offloading (MUAO) algorithm is designed. Simulation results show that MUAO consistently exhibits lower latency and energy consumption compared to the baseline scheme and other existing algorithms, while maintaining a low packet loss rate of only 5%.

In the context of multicast Network Function Virtual(NFV)-enabled Internet of Things (IoT), numerous sensor devices must efficiently transmit data to multiple data centers for real-time monitoring and analysis. Certain data centers require aggregated data from multiple sensors for informed decision-making. Outdated information lead to incorrect decisions, resulting in economic losses. Therefore, ensuring the timely and effective delivery of information is of paramount importance. However, the issue of information freshness in multicast networks has received limited attention. The deployment Virtual Network Functions (VNFs), data scheduling, and the multitude of routing possibilities pose significant challenges to studying information freshness. We introduce Mixed-Age of Aggregated Information (MAoAI) to quantify information freshness in multicast networks, integrating Age of Information (AoI) and Age of Aggregated Information (AoAI). To address this, we propose an optimization framework for coordinating multicast service requests. This framework takes into account VNF deployment and sharing, multicast routing, transmission scheduling, and data aggregation, mathematically formulated as a complex Integer Linear Programming (ILP) model. To tackle the scalability issue, we develop a Nash equilibrium-based Multicast and Scheduling Game (MSGame) approach, reducing CPU runtime by an average of 98.02% compared to ILP. Comprehensive simulations show improved solution quality and approximate optimal solutions with fewer iterations.

Uncrewed Aerial Vehicle (UAV) is extensively employed as a mobile base station in areas with inadequate cellular infrastructure to enhance the freshness of vehicle sensors. The Age of Information (AoI) is a metric utilized to characterize the freshness of information produced by vehicle sensors. This paper investigates the use of Uncrewed Aerial Vehicles (UAVs) as mobile base stations to enhance the freshness of vehicle sensor information in areas with inadequate cellular infrastructure. We focus on minimizing the Age of Information (AoI) and UAV energy consumption in a Vehicle-to-UAV (V2U) network within the Manhattan scenario. The challenge lies in jointly optimizing UAV trajectories and vehicle data packet scheduling amidst high vehicle mobility and limited communication range. To address this issue, we employ Reinforcement Learning (RL) to formulate the problem as a Markov Decision Process (MDP), proposing a Dueling Double Deep Q-Network (D3QN) method for trajectory and scheduling optimization. We also introduce Priority Experience Replay (PER) to improve reward acquisition for the UAV, addressing the issue of sparse rewards due to the expansive space for vehicle movement. Simulation results provide empirical evidence supporting the efficacy of the proposed algorithm in comparison to baseline policies.

The emergence of intelligent mobile technologies and the widespread adoption of 5G wireless networks have made Federated Learning (FL) a promising method for protecting privacy during distributed model training. However, traditional FL frameworks rely on static aggregators such as base stations, encountering obstacles such as increased energy demands, frequent disconnections, and poor model performance. To address these issues, this paper investigates an innovative aUtonomous Aerial Vehicle (UAV)-assisted FL framework, aiming to utilize UAVs as mobile model aggregators to collaborate with devices in training models, while minimizing the total energy consumption of devices and ensuring that FL can achieve the target model accuracy. By adopting the Distributed Approximate NEwton (DANE) method for local optimization, we analyze the convergence of FL and derive device scheduling constraints that aid in convergence. Accordingly, we formulate a problem of minimizing the total energy consumption of devices, integrating a constraint on global model accuracy, and jointly optimizing the UAV trajectory, device scheduling, bandwidth allocation, time slot lengths, as well as the uplink transmission power, CPU frequency, and local convergence accuracy. Then, we decompose this non-convex optimization problem into three subproblems and propose an iterative algorithm based on Block Coordinate Descent (BCD) with convergence guarantee. Simulation results indicate that, compared with various benchmark methods, our proposed UAV-assisted FL framework significantly reduces the total energy consumption of devices and achieves an improved trade-off between energy and convergence accuracy.

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.

Timely gathering of sensing data is critical in wireless sensor networks (WSNs). However, in delay-sensitive applications, maintaining the freshness of collected data poses a significant challenge. To tackle this issue, an age of information (AoI)-aware data collection method leveraging unmanned aerial vehicle (UAV) and intelligent reflective surface (IRS) is proposed in this work. Particularly, a UAV is employed to traverse over ground sensor nodes (SNs) and reliably collect their sensing data where the received signal strength is enhanced through IRS. The UAV’s flight trajectory and its association with SNs, as well as the IRS phase control strategy are jointly optimized to minimize the weighted sum of the average AoI of the SNs and energy consumption of the UAV. However, this optimization is complicated by potential inaccuracies in IRS channel state estimation. To tackle this challenge, we propose an enhanced deep reinforcement learning (DRL) framework that incorporates a dual-network agent with two nested neural networks (NNs): UAV-NN, which jointly optimizes the UAV trajectory and SN association, and IRS-NN, which dynamically adjusts IRS phase shifts based on sampled channel states, UAV position, and associated SN. By integrating this architecture into proximal policy optimization (PPO) and deep Q-network (DQN), we develop two novel algorithms: PPO-RAC and DQN-RAC, tailored for IRS-assisted UAV data collection. Extensive simulations validate their effectiveness across diverse scenarios, demonstrating significant AoI reduction compared to baseline methods.

Computational offloading in mobile edge computing (MEC) systems provides an efficient solution for resource‐intensive applications on devices. However, the frequent communication between devices and edge servers increases the traffic within the network, thereby hindering significant improvements in latency. Furthermore, the benefits of MEC cannot be fully realized when the communication link utilized for offloading tasks experiences severe attenuation. Fortunately, reconfigurable intelligent surfaces (RISs) can mitigate propagation‐induced impairments by adjusting the phase shifts imposed on the incident signals using their passive reflecting elements. This paper investigates the performance gains achieved by deploying multiple RISs in MEC systems under energy‐constrained conditions to minimize the overall system latency. Considering the high coupling among variables such as the selection of multiple RISs, optimization of their phase shifts, transmit power, and MEC offloading volume, the problem is formulated as a nonconvex problem. We propose two approaches to address this problem. First, we employ an alternating optimization approach based on semidefinite relaxation (AO‐SDR) to decompose the original problem into two subproblems, enabling the alternating optimization of multi‐RIS communication and MEC offloading volume. Second, due to its capability to model and learn the optimal phase adjustment strategies adaptively in dynamic and uncertain environments, deep reinforcement learning (DRL) offers a promising approach to enhance the performance of phase optimization strategies. We leverage DRL to address the joint design of MEC‐offloading volume and multi‐RIS communication. Extensive simulations and numerical analysis results demonstrate that compared to conventional MEC systems without RIS assistance, the multi‐RIS‐assisted schemes based on the AO‐SDR and DRL methods achieve a reduction in latency by 23.5% and 29.6%, respectively.

Unmanned aerial vehicles (UAVs) offer exceptional maneuverability and mobility, making them valuable for data collection in the Internet of Things (IoT). However, to ensure sustainable data services, UAVs with limited battery capacity require energy replenishment during their operational period. In this study, we investigate the joint design of charging station (CS) placement and UAV trajectory to enable continuous and timely data gathering in IoT networks. We formulate a mixed combinatorial optimization problem aimed at minimizing the network’s peak age of information (AoI) by deploying a specific number of CSs from a set of potential sites and designing the UAV trajectory for data gathering and energy recharging. Convex optimization techniques are employed to find the optimal UAV trajectory, given any feasible CS placement solution. Furthermore, we demonstrate that, with the optimized UAV trajectory, the optimal CS placement problem becomes a maximization problem of a non-submodular, non-decreasing set function under a cardinality constraint, known to be NP-hard. To tackle this challenge, we propose a greedy CS deployment algorithm that provides an approximate optimal solution within a constant factor of 1α1-(1-αγK)K, where α ϵ [0,1] represents the generalized curvature, γ ϵ [0,1] denotes the submodularity ratio, and K represents the number of CSs. Additionally, we introduce a low-complexity CS placement algorithm based on path allocation, which is particularly useful in scenarios involving UAVs with very limited battery capacity. Through simulation results, we demonstrate that our proposed approaches, which jointly optimize CS placement and UAV trajectory, achieve significantly smaller AoI values compared to distance-based strategies, both with and without UAV trajectory optimization.

Network Function Virtualization (NFV) is considered one of the key technologies of 5G/B5G because of its advantages of flexibility, scalability, and manageability. In NFV networks, the flow of network service needs to go through a certain number of Virtual Network Functions (VNFs) which form Service Function Chain (SFC). Compared to link protection in traditional networks, the backup transmission links for different types of VNFs need to be considered to improve the SFCs’ reliability, since any failure of transmission link may interrupt the network service. Due to the uncertainty of VNF placement and routing, the flexible selection of link backup for each VNF to satisfy the reliability requirement of SFC becomes a remarkably challenging problem. In this paper, a Flexible virtual Link Protection (Fle_LP) mechanism is proposed to calculate backup resources accurately, enhancing the reliability of NFV-enabled network service. We mathematically formulate the problem as a Mixed Integer Nonlinear Program (MINLP). An Extended Least Square (ELS) method is introduced to deal with the nonlinear constraints, which transforms MINLP to Mixed Integer Linear Programming (MILP). Owing to the MILP’s remarkable complexity, a Column Generation-based Link Protection (CG_LP) algorithm is proposed, which generates an acceptable sub-optimal solution. Numerical results show that CG_LP reduces the computing time (8-node network: 92.3 %, 16-node network: 99.6 %) while achieving the same bandwidth consumption as MILP.

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.

One-way Wave Equation Migration (OWEM) is a classic seismic imaging method offering a good trade-off between quality and compute cost in most geological cases. In recent years, GPU-based heterogeneous architecture has gained popularity for seismic imaging. In this paper, we present a generic design for asynchronous processing and data management. By applying this design, we present an efficient GPU implementation of OWEM combining OpenACC and CUDA. Our approach improves upon classic designs by exploring asynchronous compute and data transfer between CPU and GPU using high-speed NVLink, completely masking the cost of MPI communications and I/O. Using 3, 01S GPUs, our fine-tuned OWEM can process 11, 172 seismic shots in less than 75 minutes. By tuning CPU and GPU clock frequencies, we achieve around 30% energy saving with only 4% loss of performance on PANGEA III supercomputer. We believe our design combined with the energy-aware tuning will be beneficial to many GPU applications.

Telecommunication networks are currently realizing more-huge-than-ever data demands from subscribers all over the world. Due to the ongoing pandemic, nearly all businesses have adapted working models with remote operations. People engaged with major industries, e.g., academia, health and municipalities are utilizing online platforms to carryout their routine tasks. This indeed shifts the attention from one-to-one (unicast) communication to one-to-many (multicast) and many-to-many (multi-source multi-destination) communications. Network operators are facing increased pressure to provide quick responses in order to satisfy the bandwidth hungry and time sensitive user demands. This can only be done by enhancing deployability as well as manageability of the services. Network Function Virtualization (NFV) provides a transformation of traditional proprietary network designs to a more agile and software based environment in order to achieve flexible deployments, reduced setup costs and less-time-to-market for the new services which is very much needed in the current scenarios. Previous studies on NFV-enabled multicast problem either proposed Integer Linear Program (ILP) models, that are pretty unscalable, or heuristic-based techniques that do not guarantee good quality of the solutions obtained. In this article, we propose an NFV multicast resource optimization model exploiting the use of multiple sources and considering the end-to-end delay and bandwidth requirements. Herein, we propose a novel Dantzig-Wolfe (DW) decomposition model that tackles the complexity of the problem by breaking it down into a master problem and several pricing problems. We compare the DW approach with the ILP and heuristic methods and demonstrate that our approach achieves near to optimal solution (in comparison to heuristic based methods) much faster than ILP. We also study the dynamic admission of NFV-enabled multicast requests by solving the problem in an online manner using the batch processing of requests. We then evaluate the performance of the proposed algorithms through extensive simulations and demonstrate that proposed algorithms are promising and outperform existing solutions.

Network Function Virtualization (NFV) is a transformation of traditional proprietary network designs to a more agile and software based environment. NFV architecture is considered as a key enabler for 5G as it offers the flexible deployment, reduced setup costs and less-time-to-market for the new services. Current studies on NFV in unicast transmission case can not be extended to multicast. Owing to the recent popularity and growing interest for live video streaming applications, efficient multicast solutions in NFV-enabled networks are needed. In this paper, we propose an NFV multicast resource optimization model as a Mixed Integer Linear Program (MILP) exploiting the use of multiple sources and considering the end-to-end delay and bandwidth requirements along with two heuristics algorithms. We evaluate the performance of the proposed algorithms on different network topologies. Simulation results prove that the proposed algorithms outperform the existing solution in terms of reduced bandwidth consumption and the delay values.

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.

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.

The fifth generation (5G) of cellular networks is emerging as the key enabler of killer real-time applications, such as tactile Internet, augmented and virtual reality, tele-driving, autonomous driving, etc., providing them with the much needed ultra-reliable and ultra-low latency services. Such applications are expected to take full advantages of recent developments in the areas of cloud and edge computing, and exploit emerging industrial initiatives such as Software Defined Networks (SDN) and Network Function Virtualization (NFV). Often, these 5G applications require network functions (e.g., IDSs, load balancers, etc.) to cater for their end-to-end services. This paper focuses on chaining network functions and services for these applications, and in particular considers those delay sensitive ones. Here, we account for services with deadlines and formulate the joint problem of network function mapping, routing and scheduling mathematically and highlight its complexity. Then, we present an efficient method for solving these sub-problems sequentially and validate its performance numerically. We also propose and characterize the performance of a Tabu search-based approach that we design to solve the problem. Our numerical evaluation reveals the efficiency of our sequential method and the scalability of our Tabu-based algorithm.

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.

To accelerate the implementation of network functions/middle boxes and reduce the deployment cost, recently the concept of Network Function Virtualization (NFV) has emerged and became a topic of much interest attracting the attention of researchers from both industry and academia. Unlike the traditional implementation of network functions, a software-oriented approach for network functions create more flexible and dynamic network services to meet a more diversified demand. In this paper, we study the Virtual Network Function (VNF) chaining scheduling problem with limited network resources. We consider VNF transmission and processing delays, and formulate the VNFs chaining scheduling as a new Mixed Integer Linear Programming (MILP) problem. Our objective is to minimize the latency of the overall VNFs' schedule. Reducing the scheduling latency enables cloud operators to service (and admit) more customers, thereby increasing operators' revenues. Owing to the complexity of the problem, we develop a Genetic Algorithm (GA) based method for solving the problem efficiently. Finally, the effectiveness of our heuristic algorithm is verified through numerical results.

To accelerate the implementation of network functions/middle boxes and reduce the deployment cost, recently, the concept of network function virtualization (NFV) has emerged and become a topic of much interest attracting the attention of researchers from both industry and academia. Unlike the traditional implementation of network functions, a software-oriented approach for virtual network functions (VNFs) creates more flexible and dynamic network services to meet a more diversified demand. Software-oriented network functions bring along a series of research challenges, such as VNF management and orchestration, service chaining, VNF scheduling for low latency and efficient virtual network resource allocation with NFV infrastructure, among others. In this paper, we study the VNF scheduling problem and the corresponding resource optimization solutions. Here, the VNF scheduling problem is defined as a series of scheduling decisions for network services on network functions and activating the various VNFs to process the arriving traffic. We consider VNF transmission and processing delays and formulate the joint problem of VNF scheduling and traffic steering as a mixed integer linear program. Our objective is to minimize the makespan/latency of the overall VNFs' schedule. Reducing the scheduling latency enables cloud operators to service (and admit) more customers, and cater to services with stringent delay requirements, thereby increasing operators' revenues. Owing to the complexity of the problem, we develop a genetic algorithm-based method for solving the problem efficiently. Finally, the effectiveness of our heuristic algorithm is verified through numerical evaluation. We show that dynamically adjusting the bandwidths on virtual links connecting virtual machines, hosting the network functions, reduces the schedule makespan by 15%-20% in the simulated scenarios.

The performance of wireless networks depends on the achievable channel capacity for each transmission link as well as the level of spectrum spatial reuse in the network. For the latter one, successive interference cancellation (SIC) has emerged as an advanced PHY technique with the ability of decoding two or more overlapping signals, allowing multiple concurrent transmissions. In this paper, we seek to understand the benefits of SIC and its interference management capabilities in a multirate multihop wireless network. To characterise the network performance, we formulate the joint routing and scheduling problem with rate control as a mixed integer linear program (ILP) with the objective to maximize the minimum flow throughput. Given its large scale and combinatorial complexity, we follow a decomposition approach using column generation to solve the problem. We also develop one heuristic based on simulated annealing for solving efficiently the pricing subproblem. Our results indicate that SIC benefits strongly depend on the strength of the received signals. We show that transmission links with fixed higher data rates do not necessarily yield higher SIC gains because higher transmission rates results in sparser network topologies and thus less flexible routing. Larger networks with SIC capabilitities and bitrate adaptation however are most effective in controlling the interference and improving the spatial reuse and thus reaping the largest benefits.

This paper considers the problem of link scheduling in wireless networks with interference cancellation (IC) capabilities and under the physical SINR interference model. We first present a cross layer formulation and then use duality theory to decompose the joint design problem into congestion control and routing/scheduling subproblems, which interact through congestion prices. Given that the problem of scheduling with IC and under the SINR interference regime has been shown to be NP-complete, this paper develops a decentralized approach which allows links to coordinate their transmissions and therefore efficiently solving the link scheduling problem. We show that our decentralized algorithm achieves very close performance to other centralized methods (e.g., greedy maximal scheduling). We also study the performance gains that IC brings to wireless networks and we show that flows in the network achieve up to twice their rates in most instances, in comparisons with networks without interference cancellation capabilities. These gains are attributed to the capabilities of SIC in better managing the interference in the network and promoting higher spatial reuse among contending links.

The performance of wireless multihop networks depends on the achievable channel capacity for each transmission link as well as the level of spectrum spatial reuse in the network. For the latter one, successive interference cancellation (SIC) has emerged as an advanced PHY technique with the ability of decoding two or more overlapping signals and therefore allowing multiple concurrent transmissions. Effectively managing the transmission concurrency over the shared medium ensures good quality of transmission and therefore results in higher achievable transmission data rates. In this paper, we seek to understand the benefits of SIC and its interference management capabilities in a multi-rate multihop wireless network. To characterize the network performance under these characteristics, we follow a cross-layer design approach and formulate the joint routing and scheduling problem with rate control as a mixed integer linear program with the objective to maximize the minimum flow throughput. Given its large scale and combinatorial complexity, we follow a decomposition approach using column generation to solve the problem. However, the complexity of solving exactly the pricing subproblem limits the application of the model to very small size network instances. We develop one efficient greedy method for solving exactly the pricing subproblem as well as a simulated annealing based heuristic approach with very good performance. Our results indicate that SIC benefits strongly depend on the strength of the received signals. We show that transmission links with fixed higher data rates do not necessarily yield higher SIC gains because higher transmission rates result in sparser network topologies and thus less flexible routing. Larger networks with SIC capabilities and bitrate adaptation however are most effective in controlling the interference and improving the spatial reuse and thus reap the largest benefits with gains exceeding 20% over networks only with SIC capabilities or only with rate control.

We present the parallel design and performance of the nested filtering factorization preconditioner (NFF), which can be used for solving linear systems arising from the discretization of a system of PDEs on unstructured grids. NFF has limited memory requirements, and it is based on a two level recursive decomposition that exploits a nested block arrow structure of the input matrix, obtained priorly by using graph partitioning techniques. It also allows to preserve several directions of interest of the input matrix to alleviate the effect of low frequency modes on the convergence of iterative methods. For a boundary value problem with highly heterogeneous coefficients, discretized on three-dimensional grids with 64 millions unknowns and 447 millions nonzero entries, we show experimentally that NFF scales up to 2048 cores of Genci's Bull system (Curie), and it is up to 2.6 times faster than the domain decomposition preconditioner Restricted Additive Schwarz implemented in PETSc.

This study examined disease models most indicative of risk of Long QT Syndrome (LQTS) in children. Data mined from electronic health records of children confirmed with (n=248) and without (n=101) a diagnosis of LQTS were used to develop a patient profile for LQTS. The profile consisted of 44 distinct features, 17 of which were enriched in LQTS patients. Notably, 66.9% of subjects with a diagnosis of LQTS fell into a category of “low” (22.6%) or “intermediate” (44.3%) risk using a current LQTS risk assessment standard. We developed and trained a machine learning process for LQTS classification by applying a Naïve Bayes model to our LQTS cohort. The model classified patients with a sensitivity of 91.1% and a specificity of 73.3%. These results suggest that data mining of clinical data in conjunction with a Bayesian modeling approach can lead to a diagnostic system for prediction of LQTS in children.