Search

In this work, we investigate the issue of semantics in non real-time communication scenarios, that is, without stringent information delivery constraints. While semantics has been widely used in the context of communications, the definition of the semantic metrics has been limited to the real-time applications. We thus revise the basic concepts of the timeliness and the accuracy and propose their formal definitions in the case of non real-time applications characterized by a possibility of non-causal signal reconstruction at the destination. In particular, we link the concept of accuracy to the canonical first-order auto-regressive model of the signal, while the timeliness is defined by the time difference to reference samples of the signal. We compare the proposed semantic metrics in the case of different buffer management strategies and using numerical examples we show that the optimality of the transmission strategy changes when the real-time or non real-time applications are considered. [ABSTRACT FROM AUTHOR]

Fully automated vehicles deployed with high computational/perceptive capabilities will soon become a reality. Such capabilities enable the cooperation among vehicles and the realization of interacting autonomous driving systems. Edge computing has emerged to provide a plethora of computational services to reduce network latency. Applications at the edge that apply analytics on the sensory data are therefore indispensable for self-driving vehicles. We consider in this paper a network that interconnects vehicles to an edge server at a roadside unit. Each vehicle extracts multiple information by sampling multiple processes and sends them to the corresponding edge application. To make timely decisions, “fresh” information needs to be offloaded, processed, and delivered back to vehicles; in this context, we adopt a new metric called Age of Information (AoI) that has been lately used to measure the freshness of information. We seek to jointly schedule vehicles’ transmission of information and schedule information processing at the edge to minimize the AoI of all processes. We mathematically formulate the problem and prove its NP-Hardness. To overcome this hardness, we propose a logic-based Benders decomposition to divide the problem into a master and several subproblems. Then, we present an exact polynomial-time solution for the subproblems, a scalable heuristic for the master, and devise a valid yet efficient Benders cut. We implement the system simulation on the well-known traffic simulator SUMO and compare the decomposition with CPLEX branch-and-cut; Although the problem is highly intricate, our method finds a near-optimal solution (maximum deviation is 7% from optimal solution) with a speedup that reaches 95%. We study the system performance by varying different system parameters. [ABSTRACT FROM AUTHOR]

Unmanned Aerial Vehicles (UAVs) are gaining growing interests due to the paramount roles they play, particularly these days, in enabling new services that help modernize our transportation, supply chain, search and rescue, among others. They are capable of positively influencing wireless systems through enabling and fostering emerging technologies such as autonomous driving, vertical industries, virtual reality and so many others. The Internet of Vehicles is a prime sector benefiting from the services offered by future cellular systems in general and UAVs in particular, and this paper considers the problem of content delivery to vehicles on road segments with either overloaded or no available communication infrastructure. Incoming vehicles demand service from a library of contents that is partially cached at the UAV; the content of the library is also assumed to change as new vehicles carrying more popular contents arrive. Each inbound vehicle makes a request and the UAV decides on its best trajectory to provide service while maximizing a certain operational utility. Given the energy limitation at the UAV, we seek an energy efficient solution. Hence, our problem consists of jointly finding caching decisions, UAV trajectory and radio resource allocation which is formulated mathematically as a Mixed Integer Non-Linear Problem (MINLP). However, owing to uncertainties in the environment (e.g., random arrival of vehicles, their requests for contents and their existing contents), it is often hard and impractical to solve using standard optimization techniques. To this end, we formulate our problem as a Markov Decision Process (MDP) and we resort to tools such as Proximal Policy Optimization (PPO), a very promising Reinforcement Learning method, along with a set of crafted algorithms to solve our problem. Finally, we conduct simulation-based experiments to analyze and demonstrate the superiority of our solution approach compared with four counterparts and baseline schemes. [ABSTRACT FROM AUTHOR]

We investigate the benefits of integrating unmanned aerial vehicles (UAVs) with reconfigurable intelligent surface (RIS) elements to passively relay information sampled by Internet of Things devices (IoTDs) to the base station (BS). In order to maintain the freshness of relayed information, an optimization problem with the objective of minimizing the expected sum Age-of-Information (AoI) is formulated to optimize the altitude of the UAV, the communication schedule, and phases-shift of RIS elements. In the absence of prior knowledge of the activation pattern of the IoTDs, proximal policy optimization algorithm is developed to solve this mixed-integer non-convex optimization problem. Numerical results show that our proposed algorithm outperforms all others in terms of AoI. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The precision time protocol (PTP) is considered as one of the most favorable mechanisms for providing unified and precise time at the substation level in the smart grid. Nevertheless, PTP was shown to be vulnerable to cyber-attacks targeting its components and synchronization services. In this article, we capitalize on the theory and outcome of our previous work to contribute a more complete solution that addresses PTP cyber security. We propose to close the PTP loop through an extension that introduces new functionality and messages. This extension covers the PTP attack surface and enables the detection of attacks on PTP time synchronization. We formally model and verify the proposed extension using UPPAAL model checker. In addition, we validate the proposed extension using Omnet++ simulation. The evaluation demonstrates that our approach preserves PTP functionality, while successfully detecting cyber attacks against PTP components in a timely manner. [ABSTRACT FROM AUTHOR]

In order to support the development of 5G technologies, researchers are actively engaged in addressing the challenges accompanying the emerging 5G applications. Unquestionably, an eminent technology gaining significant research attention is edge computing. Vehicular edge computing brings data storage and computing capabilities as well as hosting support applications that comprise emerging vehicular services and applications which demand low-delay processing, to the edge closer to the vehicles, reducing response time and increasing reliability, therefore achieving the holistic vision of the tactile Internet. In this context, this paper considers a vehicular network with edge computing capabilities deployed at road side units, and addresses the problem of workload offloading as well as scheduling of computation tasks on the computing resources available at the edge. The challenge here is the high mobility of the vehicles and hence their short residence time within the coverage range of the road side units hosting the edge computing resources. A joint problem considering the communication and computation resources, as well as the latency requirements of the workload is formulated and the scheduling is shown to be NP-Hard. Subsequently, efficient solutions based on Lagrangian relaxation are derived and presented. We evaluate numerically the proposed methods and show their closeness to the optimal solutions. [ABSTRACT FROM AUTHOR]

Vehicular networks’ connectivity and data delivery delay performance is highly affected by the vehicular traffic's spatio-temporal dynamics whose variations are subject to a multitude of random factors. Under the stringent and inevitable limitations imposed by free-flow vehicular traffic conditions (i.e., low-to-medium vehicular densities, elevated degree of mobility, high speeds, etc), these networks suffer from considerably rapid topology variations leading to severe connectivity intermittence and, hence, delayed data delivery. This motivates the study presented in this paper, which aims at investigating the capability of external elements that are independent of the vehicular traffic flow and its inherent limitations (e.g., airborne unmanned aerial vehicles (UAVs), a.k.a., drones) to serve as possible adjuvant relays; thus, contributing to strengthening/healing weak/broken communication links among ground-bound vehicular entities (i.e., RoadSide Units (RSUs) and vehicles) and uplifting the vehicular connectivity and delay performance. Particularly, in the context of a vehicular sub-networking scenario, a UAV mobility model is proposed as a first step in analytically capturing macroscopic dynamics for UAVs exhibiting waypoint mobility patterns and plying over a considered roadway segment. Then, a stochastic analytical model is formulated for the purpose of mathematically characterizing the path availability and achieved data delivery delays in the presence of these UAVs. A simulation framework is established to verify the validity and accuracy of the proposed models and gauge the merit of UAV assistance in improving the vehicular connectivity and data delivery delay performance. [ABSTRACT FROM AUTHOR]

The proliferation of smart connected Internet of Things (IoT) devices is bringing tremendous challenges in meeting the performance requirement of their supported real-time applications due to their limited resources in terms of computing, storage, and battery life. In addition, the considerable amount of data they generate brings extra burden to the existing wireless network infrastructure. By enabling distributed computing and storage capabilities at the edge of the network, multi-access edge computing (MEC) serves delay sensitive, computationally intensive applications. Managing the heterogeneity of the workload generated by IoT devices, especially in terms of computing and delay requirements, while being cognizant of the cost to network operators, requires an efficient dimensioning of the MEC-enabled network infrastructure. Hence, in this paper, we study and formulate the problem of MEC resource provisioning and workload assignment for IoT services (RPWA) as a mixed integer program to jointly decide on the number and the location of edge servers and applications to deploy, in addition to the workload assignment. Given its complexity, we propose a decomposition approach to solve it which consists of decomposing RPWA into the delay aware load assignment sub-problem and the mobile edge servers dimensioning sub-problem. We analyze the effectiveness of the proposed algorithm through extensive simulations and highlight valuable performance trends and trade-offs as a function of various system parameters. [ABSTRACT FROM AUTHOR]

Smart grid is a typical cyber-physical system, which presents the dependence of power system operations on cyber infrastructure for control, monitoring, and protection purposes. The rapid deployment of synchrophasor measurements units (PMUs) supporting wide area measurement system (WAMS) in the smart grid transmission system has opened opportunities to enhance the grid operations through the introduction of WAMS applications. However, the increased deployment of synchrophasor technologies increases the effective attack surface available to attackers and exposes WAMS applications. Such applications have strict and stringent delay requirements, e.g., end-to-end delay as well as delay variation between measurements from different PMUs. In this paper, we present a mathematical model for constructing forwarding trees for PMUs’ measurements which satisfy the end-to-end delay as well as the delay variation requirements of WAMS applications at data concentrators. We illustrate that simple shortest path routing will result in larger fraction of data drop and that our method will achieve better delivery rate. We also validate our method against delay attacks using real-time co-simulation. [ABSTRACT FROM AUTHOR]

In this paper, we consider a novel model of two-tier wireless backhaul (WB) small cell networks where each small cell access point is equipped with a buffer of finite storage. We employ a time-spectrum transmission accommodation scheme to separate the WB and access transmissions on top of developing a joint optimized transmit beamforming and power allocation algorithm. Unlike previous work, we propose a more advanced buffering protocol to study the small cell performance improvement by solving the offline and online optimization problems that maximize the total small cell access rate. Both kind of problems are generally non-convex and NP-hard. The offline scheme assumes the availability of channel state information (CSI) in current and future time slots and is used as a benchmark for online algorithms. To solve it, we develop a low-complexity algorithm which iteratively solves a sequence of lower bounded convex approximated problems until convergence. The formulated online problems must be solved in a slot-by-slot manner since transmitters only know the CSI of the current time slot. Then, we also develop low-complexity online algorithms to jointly design the transmit beamforming and power allocation. Our theoretical and numerical simulation results show that our proposed model with advanced buffering strategy outperforms the conventional designs in terms of small cell access rate for both offline and online algorithms. [ABSTRACT FROM AUTHOR]

Smart grid applications demand the availability of a reliable and accurate time signal. Measurements and events need to be correctly aligned to enable proper actions and decisions. Precision time protocol (PTP) is the favored protocol for time distribution across smart grid domains. The correct functionality of PTP is of paramount importance and its security is of high priority. To harden its security, detection, and prevention mechanisms against attacks targeting PTP are needed. In this paper, we propose detection and mitigation mechanisms against the known PTP delay attack. We apply model checking to quantify the effect of the delay attack. Moreover, the validity of the proposed mechanism is formally proven. The suggested approach is tested on a physical system. The collected results support the usefulness of the mechanism in detecting the delay attacks targeting PTP, and preserving the system functionality. [ABSTRACT FROM AUTHOR]

Middleboxes (i.e., firewall, cache, proxy, etc.) are hardware appliances designed to enforce security and performance policies. Being an integral part of today’s cloud and enterprise networks, these middleboxes are expensive, hard to manage and to maintain. Network function virtualization has emerged as a promising technology that replaces these hardware appliances by software ones known as virtual network functions (VNFs). Unlike hardware middleboxes, VNFs can be instantiated and deployed on virtual machines running on commodity servers which ensures their flexibility, manageability, cost-efficiency, and reduce their time-to-market. However, efficiently processing services through an ordered chain of VNFs, called service function chaining (SFC), is not trivial. It requires solving three inter-related sub-problems; the network functions (NFs) mapping sub-problem, the traffic routing sub-problem and the service scheduling sub-problem. This paper first highlights the existing interplay between the three sub-problems and then presents a formulation of the SFC scheduling (SFCS) which exploits interactions between NFs mapping onto VNFs, service scheduling and traffic routing. Given the complexity of the SFCS problem, we present a novel primal–dual decomposition using column generation that solves exactly a relaxed version of the problem and can serve as a benchmark approach. We enhance our solution methodology with a diversification technique to help improve the quality of the obtained solutions. We evaluate numerically our method and show that it can attain optimal solutions substantially faster. Finally, we present several engineering insights for improving the network performance. [ABSTRACT FROM PUBLISHER]

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. [ABSTRACT FROM PUBLISHER]

In typical wireless networks, multihop communication is a method used to establish connectivity between distant nodes. Adapting this technique to vehicular networks requires bypassing several challenging constraints imposed by the nature of vehicular environments (e.g., high mobility and speeds and repetitive link disruptions). This paper revolves around establishing a multihop connectivity path between an isolated source vehicle $S$ and a faraway gateway roadside unit (RSU) $D$ through cooperative vehicles serving as intermediate relays. A stochastic model is formulated for the purpose of deriving an expression for the probability of the existence of a connectivity path between $S$ and $D$. Then, the dynamic changes in the network topology are carefully examined to present a tight upper bound for the average end-to-end packet delivery delay. Finally, taking into account the inherent contention-based nature of the employed IEEE 802.11p medium access control (MAC) protocol, together with several other limiting factors such as relay unavailability and hidden terminals, the per-hop and the end-to-end throughput expressions are presented. Extensive simulations are conducted for the purpose of validating the proposed model and examining the system's performance. [ABSTRACT FROM PUBLISHER]

This paper deals with the multiple link/node substrate failures that impact a multicast virtual network (MVN) in which link recovery is not feasible and node migration is mandatory. A novel restoration approach is introduced to repair the failed MVNs while maintaining their quality of service requirements (e.g., end-to-end delay and delay variations). This approach relies on reducing the search region and exploiting nodes ranking and filtering (NRF) techniques to speed up the recovery process of finding an alternative node to which to migrate. The performance is extensively evaluated against multiple failures, with and without NRF, compared with complete re-embedding technique, link failure algorithms for single link failure, and previous work for single node failure. Simulation results prove that our recovery technique achieves good restoration ratio in considerably fast execution time, low link mapping cost (gain) with a slight impact on the admission ratio. [ABSTRACT FROM PUBLISHER]

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. [ABSTRACT FROM PUBLISHER]

The failure-prone nature of data center networks has evoked countless contributions to develop proactive and reactive countermeasures. Yet, most of these techniques were developed with unicast services in mind. When in fact, multiple services hosted in data center networks today rely on multicast communication to disseminate traffic. Hence, the existing survivability schemes fail to cater to the distinctive properties and quality of service requirements that multicast services entail. This letter is devoted to understanding the ramifications of facility node or substrate link failure on multicast services residing in cloud networks. We formally define the multicast virtual network restoration problem and prove its NP-complete nature in arbitrary graphs. Furthermore, we prove that the problem can be solved in polynomial-time in multi-rooted treelike data center network topologies. [ABSTRACT FROM PUBLISHER]

Network virtualization is regarded as the pillar of cloud computing, enabling the multi-tenancy concept where multiple Virtual Networks (VNs) can cohabit the same substrate network. With network virtualization, the problem of allocating resources to the various tenants, commonly known as the Virtual Network Embedding problem, emerges as a challenge. Its NP-Hard nature has drawn a lot of attention from the research community, many of which however overlooked the type of communication that a given VN may exhibit, assuming that they all exhibit a one-to-one (unicast) communication only. In this paper, we motivate the importance of characterizing the mode of communication in VN requests, and we focus our attention on the problem of embedding VNs with a one-to-many (multicast) communication mode. Throughout this paper, we highlight the unique properties of multicast VNs and its distinct Quality of Service (QoS) requirements, most notably the end-delay and delay-variation constraints for delay-sensitive multicast services. Further, we showcase the limitations of handling a multicast VN as unicast. To this extent, we formally define the VNE problem for Multicast VNs (MVNs) and prove its NP-Hard nature. We propose two novel approach to solve the Multicast VNE (MVNE) problem with end-delay and delay variation constraints: A 3-Step MVNE technique, and a Tabu-Search algorithm. We motivate the intuition behind our proposed embedding techniques, and provide a competitive analysis of our suggested approaches over multiple metrics and against other embedding heuristics. [ABSTRACT FROM PUBLISHER]