Blocking Probability Research Articles

Spectrum slicing-based fragmentation aware routing and spectrum allocation in elastic optical networks

For addressing the problems of high bandwidth blocking probability and low spectrum utilization caused by spectrum fragmentation in elastic optical networks (EONs), a dynamic EONs node architecture for deploying spectrum slicer is studied to transmit the request in the paper. The spectrum slicer can relax spectrum contiguity and improve spectrum fragmentation utilization for request's lightpath establishing. Then, a fragmentation aware routing and spectrum allocation based on spectrum slicing algorithm (SS-FA-RSA) is put forward. In the routing selection phase of SS-FA-RSA, a path weight value formula is designed to trade-off between path length, normalized fragmentation rate and available spectrum slicer, which reorder the candidate paths to select the most appropriate routing path for the request. Moreover, in the spectrum allocation phase, the spectrum slicing algorithm is proposed to increase the utilization of spectrum fragmentation by reusing spectrum fragmentation in EONs. Comparing with other algorithms, the simulation results show the proposed SS-FA-RSA can get better in bandwidth blocking probability and spectrum utilization.

Closed-Form UAV LoS Blockage Probability in Mixed Ground- and Rooftop-Mounted Urban mmWave NR Deployments.

Unmanned aerial vehicles (UAV) are envisioned to become one of the new types of fifth/sixth generation (5G/6G) network users. To support advanced services for UAVs such as video monitoring, one of the prospective options is to utilize recently standardized New Radio (NR) technology operating in the millimeter-wave (mmWave) frequency band. However, blockage of propagation paths between NR base stations (BS) and UAV by buildings may lead to frequent outage situations. In our study, we use the tools of integral geometry to characterize connectivity properties of UAVs in terrestrial urban deployments of mmWave NR systems using UAV line-of-sight (LoS) blockage probability as the main metric of interest. As opposed to other studies, the use of the proposed approach allows us to get closed-form approximation for LoS blockage probability as a function of city and network deployment parameters. As one of the options to improve connectivity we also consider rooftop-mounted mmWave BSs. Our results illustrate that the proposed model provides an upper bound on UAV LoS blockage probability, and this bound becomes more accurate as the density of mmWave BS in the area increases. The closed-form structure allows for identifying of the street width, building block and BS heights, and UAV altitude as the parameters providing the most impact on the considered metric. We show that rooftop-mounted mmWave BSs allow for the drastic improvement of LoS blockage probability, i.e., depending on the system parameters the use of one rooftop-mounted mmWave BS is equivalent to 6–12 ground-mounted mmWave BSs. Out of all considered deployment parameters the street width is the one most heavily affecting the UAV LoS blockage probability. Specifically, the deployment with street width of 20 m is characterized by 50% lower UAV LoS blockage probability as compared to the one with 10 m street width.

The RapidIO Routing Strategy Based on the Double-Antibody Group Multi-Objective Artificial Immunity Algorithm.

The RapidIO standard is a packet-switching interconnection technology similar to the Internet Protocol (IP) conceptually. It realizes the high-speed transmission of RapidIO packets at the transport layer, but this greatly increases the probability of network blocking. Therefore, it is of great significance to optimize the RapidIO routing strategy. For this problem, this paper proposes a Double-Antibody Group Multi-Objective Artificial Immune Algorithm (DAG-MOAIA), which improves the local search and global search ability of the population by adaptive crossover and adaptive mutation of the double-antibody groups, and uses co-competition of multi-antibody groups to increase the diversity of population. Through DAG-MOAIA, an optimal transmission path from the source node to multiple destination nodes can be selected to solve the Quality Of Service (QoS) problem during data transmission and ensure the QoS of the RapidIO network. Simulation results show that DAG-MOAIA could obtain high-quality solutions to select better routing transmission paths, and exhibit better comprehensive performance in all simulated test networks, which plays a certain role in solving the problem of the RapidIO routing strategy.

Multicast traffic grooming in elastic optical network under Dynamic scenario

Elastic optical networks allow elastic allocation and de-allocation of optical resources to optimize network resources and reduce traffic demand blocking probability for dynamic traffic demands. The routing of dynamic traffic demands is a challenging task since the traffic demands are not predefined, they arrive and leave randomly. The approach presented here is a grooming, routing and spectrum allocation technique for multicast traffic demands in elastic optical networks for dynamic type of traffic demands. The simulation results show reduced blocking probability compared to existing approach.

Dynamic Routing and Spectrum Assignment Based on the Consecutive Sub-Channels in Flexible-Grid Optical Networks

Using Optical Orthogonal Frequency Multiplexing (O-OFDM), variable bandwidth channels can be created in Elastic Optical Networks (EON). This allows the use of spectrum more efficiently by allocating integral multiple of basic bandwidth slots to the lightpath requests. Consequently, such networks are also called flexible grid optical networks. It also adds a constraint of keeping all the allocated slots together when deciding the routes for the requests. This constraint called the contiguity constraint makes the routing and spectrum algorithms more challenging. In any network, the lightpath requests will arrive and depart dynamically and will invariably lead to spectrum fragmentation, and hence network will have a reduction in maximum possible utilization due to increased blocking probability. In this paper, we have presented an improvised RSA algorithm that leads to lesser fragmentation. It is evident from the results that the presented RSA algorithm uses adaptive parameters to reduce the blocking probability and fragmentation compared to other algorithms reported in the recent past.

Fairness-Oriented Multiple RIS-Aided mmWave Transmission: Stochastic Optimization Methods

In millimeter wave (mmWave) systems, it is challenging to ensure reliable communication links due to the high sensitivity to the presence of blockages. In order to improve the robustness of mmWave systems in the presence of random blockages, we consider the deployment of multiple reconfigurable intelligent surfaces (RISs) to enhance the spatial diversity gain, and the design of robust beamforming schemes based on stochastic optimization methods that minimize the maximum outage probability among multiple users so as to ensure fairness. Under the stochastic optimization framework, we adopt the stochastic majorization–minimization (SMM) method and the stochastic successive convex approximation (SSCA) method to construct deterministic surrogate problems at each iteration, and to obtain closed-form solutions of the precoding matrix at the base station (BS) and the beamforming vectors at the RISs. Both stochastic optimization methods are proved to converge to the set of stationary points of the original stochastic problems. Simulation results show that the proposed robust beamforming for RIS-aided systems can effectively compensate for the performance loss caused by the presence of random blockages, especially when the blockage probability is high.

Coordination Protocol and Admission Control for Distributed Services in System-of-Systems With Real-Time Requirements

System-of-Systems (SoS) offer unprecedented potential for new types of emerging services, which significantly exceed the capabilities of the constituting systems. SoS in safety-critical domains (e.g., medical applications, smart grid, disaster recovery, defense) are prominent examples, but they have stringent real-time and reliability requirements. Therefore, a suitable temporal and spatial allocation of resources is required both within each constituent system and in the wide area networks between them. This paper introduces an algorithm for admission control and resources’ allocation, which considers these requirements and the autonomy of the constituent systems. To simulate a realistic admission control and resources’ allocation process of a typical SoS network, a simulated case study with eight constituent systems, six services, and twenty-five processes/requests is developed. The suggested admission control and resources’ allocation process’s performance is measured in terms of gain in the execution time and blockage probability. A sensitivity analysis is carried out to evaluate the influence of the number of constituent systems and the number of services sought by the received processes/requests on the efficacy of the proposed process. The results show that the proposed admission control and resources’ allocation process have very low blockage probability, high gain in the execution time, and high resources’ utilization.

Modeling of Multi-Service Switching Networks With Multicast Connections

This article presents an analytical model of a switching network that can be used in the nodes of a large number of modern multi-service networks. The model of a multi-service three-stage switching network proposed in this article has the advantage of providing the possibility to evaluate the point-to-point blocking probability for multicast connections. The information obtained in this way can then be used both at the stage of the estimation of the capacity of a new network and during the optimisation process of an existing network. In the calculations performed during the investigations, three scenarios for the fan out of multicast connections in the switching network are taken into consideration. The assumption is that the branching of a connection can occur in the first, second and the third stage of the switching network. The model proposed in the article is based on the concept of “effective” availability. The article discusses the methods for the determination of the effective availability parameter for successive connections that belong to a given multicast connection. The results of the analytical modelling are compared with the simulation data for three selected structures of multi-service switching networks to which different mixtures of multi-service BPP traffic are offered. The results of the calculations and the data provided by the simulation experiments validate and confirm high accuracy of the proposed model. This allows the model to be easily applied in practice to different environments to evaluate the capacities of the nodes in multi-service networks.

Performance Analysis of Random Access Mechanism in 5G Millimeter Wave Networks: Effect of Blockage, Shadowing and Mobility

5G NR has gained importance in academic and industrial research communities in recent times as its millimetre wave (mmWave) band operations offer a promising alternative for next-generation wireless communications. However, the susceptibility of mmWave signals to severe path loss and shadowing requires the use of highly directional antennas. Since the narrow beams are vulnerable to blockages, the interference behaviour becomes the key factor in building the network. In this paper, we propose a blockage model by considering the distribution of buildings in the cell as a Poisson point process. We analytically derive the blockage probability using the queuing theory. Subsequently, we derive the interference statistics using the blockage model considering the spatial randomness of the locations of blockages and User Equipments (UEs). We define and derive the coverage probability of a UE depending on the interference statistics. We calculate the success probability of a preamble transmission of a UE subjected its coverage. The effect of mobility on blockage, coverage and preamble success probability has been presented through extensive simulations.

Performance of resource delayed release strategy in software-defined OTN over WDM networks for uniform and non-uniform traffic

In today's wide area networks, especially in Optical Transport Networks (OTN) with Software Defined Networking (SDN) features enabled over Wavelength Division Multiplexing (WDM), Bandwidth on Demand (BoD) is an important service that can be satisfied by dynamic end-to-end service provisioning. Service provisioning time (SPT) and Blocking Probability (BP) are critical performance metrics for the users and carriers. This paper extends the concept of the Resource Delayed Release (RDR) strategy for WDM networks. The basic idea of this strategy is to introduce a delay in releasing the optical channel, when the channel is no longer carrying any services. This delay can help speed up the provisioning time for carrying the next service request, avoiding the time usually taken to establish a new optical channel. The main goals of the RDR method are to reduce SPT and BP while simultaneously satisfying the quality of service (QoS) constraints. In this paper, we investigate the effects of uniform and non-uniform traffic on the performance of RDR strategy. For non-uniform traffic simulation, we use a mesh topology with the 14 most populous cities in USA as of 2018 and model the non-uniform traffic based on population density. Further, we introduce a new metric called the Bandwidth Blocking Probability (BBP) to measure the quality of the service offered by the network. Simulation results show advantages of using the RDR method under a wide variety of traffic scenarios for both uniform and non-uniform traffic distributions compared to the traditional method. RDR reduces SPT by 45–90% for uniform traffic and 41–75% for non-uniform traffic. RDR reduces BP by 35–85% for uniform traffic and 30–75% for non-uniform traffic. Additionally, RDR lowers BBP by 31–73% for uniform traffic and 29–68% for non-uniform traffic.

Efficiency and fairness improvement for elastic optical networks using reinforcement learning-based traffic prediction

The characteristics of traffic in core networks have changed in recent years, with the data flows among network nodes now showing large volumes but also large fluctuations during the day. In such a dynamic environment, the static allocation of network resources by making reservations at the peak rates is wasteful, both in terms of energy consumption and resource efficiency. Periodic reconfiguration of network resources is necessary for the full exploitation of an elastic optical network’s potential. In this paper, we present a resource reconfiguration scheme for an IP over elastic optical core network. The cornerstone of the proposed scheme is a novel traffic prediction mechanism based on reinforcement learning. Using the forecasting mechanism, a heuristic routing and spectrum assignment algorithm was designed to efficiently and fairly allocate network resources periodically. Simulation results show both a reduction of the bandwidth blocking probability and an increase of the fairness index regarding the network resources allocation over corresponding algorithms in the literature.

Virtual optical network embedding of time-varying traffic in elastic optical networks

Network virtualization can enhance the flexibility of Internet infrastructure resources and the scalability of the substrate elastic optical networks (EONs) through the coexistence of multiple virtual optical networks supporting different services. Traditional virtual optical network embedding mostly assumed fixed embedding, that is, once a virtual optical network (VON) is embedded, the network resources allocated to it are almost unchanged during its lifetime. However, the actual traffic demand on a VON usually changes over time. To address the problems of high bandwidth blocking probability and low spectrum utilization caused by the mismatch between the resources allocated and the required resources during the virtual optical network embedding of the time-varying traffic in the elastic optical networks (EONs), we propose a virtual optical network embedding algorithm of the time-varying traffic (TVT-VONE). Firstly, in the node embedding stage, a node weight value measurement is designed based on the attributes of virtual nodes and physical nodes. Secondly, in the virtual optical link embedding stage, a measurement formula for path spectrum resource richness is designed to decrease link embedding failure due to insufficient spectrum resources on the path. Finally, in order to adapt to the characteristics of dynamic changes in bandwidth requirements of time-varying traffic, we design a resource-aware virtual network reconfiguration strategy when the spectrum resources of traffic required do not match the resources allocated to the changed traffic. Simulation results show that the proposed algorithm can effectively reduce the bandwidth blocking probability of VON requests and improve spectrum utilization of EONs.

Call Blocking Probabilities under a Probabilistic Bandwidth Reservation Policy in Mobile Hotspots

In this paper we study a mobility-aware call admission control algorithm in a mobile hotspot. To this end, a vehicle is considered which has an access point with a fixed capacity. The vehicle alternates between stop and moving phases. When the vehicle is in the stop phase, it services new and handover calls by prioritizing them via a probabilistic bandwidth reservation (BR) policy. Based on this policy, new handover calls may enter the reservation space with a predefined probability. When the vehicle is in the moving phase, it services new calls only. In that phase, two different policies are considered: (a) the classical complete sharing (CS) policy, where new calls are accepted in the system whenever there exists available bandwidth, and (b) the probabilistic BR policy. Depending on the selected policy in the moving phase, we propose the probabilistic BR loss model (if the CS policy is selected) and the generalized probabilistic BR loss model (if the probabilistic BR policy is selected). In both stop and moving phases, where the call arrival process is Poisson, calls require a single bandwidth unit in order to be accepted in the system, while the service time is exponentially distributed. To analytically determine call blocking probabilities and the system’s utilization, we propose efficient iterative algorithms based on two-dimensional Markov chains. The accuracy of the proposed algorithms is verified via simulation.

Multi-Agent and Cooperative Deep Reinforcement Learning for Scalable Network Automation in Multi-Domain SD-EONs

The service provisioning in multi-domain software-defined elastic optical networks (SD-EONs) is an interesting but difficult problem to tackle, because the basic problem of lightpath provisioning, i.e., the routing and spectrum assignment (RSA), is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {NP}$ </tex-math></inline-formula> -hard, and each domain is owned and operated by a different carrier. Therefore, even though numerous RSA heuristics have been proposed, there does not exist a universal winner that can always achieve the lowest blocking probability in all the scenarios of a multi-domain SD-EON. This motivates us to revisit the inter-domain provisioning problem in this paper by leveraging deep reinforcement learning (DRL). Specifically, we propose DeepCoop, which is an inter-domain service framework that uses multiple cooperative DRL agents to achieve scalable network automation in a multi-domain SD-EON. DeepCoop employs a DRL agent in each domain to optimize intra-domain service provisioning, while a domain-level path computation element (PCE) is introduced to obtain the sequence of the domains to go through for each lightpath request. By sharing a restricted amount of information among each other, the DRL agents can make their decisions distributedly. To ensure scalability and universality, we design the action space of each DRL agent based on well-known RSA heuristics, and architect the agents based on the soft actor-critic (SAC) scenario. We run extensive simulations to evaluate DeepCoop, and the results show that DeepCoop can adapt to the dynamic environment in a multi-domain SD-EON to always select the best RSA heuristic for minimizing blocking probability, and it outperforms the existing algorithms on inter-domain provisioning in various scenarios. Moreover, we verify that the distributed training implemented in DeepCoop ensures its universality and scalability (i.e., its training and operation do not depend on the topology of the SD-EON).

Processor Sharing Queues With Impatient Customers and State-Dependent Rates

We study queues with impatient customers and Processor Sharing (PS) discipline as well as other variants of PS discipline, namely, Discriminatory Processor Sharing (DPS) and Generalized Processor Sharing (GPS) disciplines, where customers have deadlines until the end of service (DES). Customers arrive according to a state-dependent Poisson process and have general impatience. Customers have exponential service times with state-dependent service rates. Analytical methods based on simple Markov chains are given for the performance analysis of such queues. The principal measures of performance are the steady-state probability of missing deadline and the steady-state probability of blocking. Similar results are obtained for related queues with Random Order Service (ROS) discipline where customers have deadlines until the beginning of service (DBS). In view of a lack of exact analytical results for First Come First Served (FCFS) queues with state-dependent rates, a highly accurate approximation method is also given for these latter queues. The efficacy and accuracy of the approach are illustrated by some numerical examples and simulation experiments.

Predicting road blockage due to building damage following earthquakes

Transportation infrastructure supports the social and economic activities of communities. One of the impacts of roads’ disruption is the obstruction of emergency services (e.g., ambulance, firefighting, evacuation). Furthermore, the recovery process of a community following an extreme event (e.g., a natural hazard) depends on the functionality of the transportation infrastructure. Therefore, conducting a risk and resilience analysis of transportation infrastructure is critical to help communities minimize the initial impact of hazards and promote a rapid recovery. Current approaches model the probability of road blockage due to building damage using high-resolution optical satellite images and aerial photographs collected after past events. However, the data used by these methods are limited, and few data have been collected before 2010. Besides, data may not be available for specific regions that have not experienced recent earthquakes. Thus, a probabilistic predictive method is needed for risk and resilience analysis of roads. This paper proposes a probabilistic model using the data from the 2010 Haiti Earthquake and calibrated by Bayesian approach to predict the debris distance from undamaged buildings (e.g., the distance debris can reach from the footprint of the undamaged building). The model is then used to construct fragility curves that give the conditional probability of road blockage at a given road section for a given seismic intensity. The proposed model considers the relevant factors affecting the road blockage probability, including building types, damage level, and road characteristics. The probability of road blockage at a given road section is estimated for the four general road section types, considering buildings on only one side of the road or both sides, and with or without a raised traffic median. The probability of road blockage for an entire road is then calculated by system and parallel reliability analysis. The proposed models apply to any general urban area without the dependence on historical data from past earthquakes.

Performance Analysis of a Cloud Computing System using Queuing Model with Correlated Task Reneging

Queuing theory has been extensively used in the modelling and performance analysis of cloud computing systems. The phenomenon of the task (or request) reneging, that is, the dropping of requests from the request queue often occur in cloud computing systems, and it is important to consider it when developing performance evaluations models for cloud computing infrastructures. Majority of studies in the performance evaluation of cloud computing data centres with the use of queuing theory do not consider the fact that the tasks could be removed from queue without being serviced. The removal of tasks from the queue could be due to the user impatience, execution deadline expiration, security reasons, or as an active queue management strategy. The reneging could be correlated in nature, that is, if a request is dropped (or reneged) at any time epoch, and then there is a probability that a request may or may not be dropped at the next time epoch. This kind of dropping (or reneging) of requests is referred to as correlated request reneging. In this paper we have modelled a cloud computing infrastructure with correlated request reneging using queuing theory. An M/M/1/N queuing model with correlated reneging has been used to study the performance analysis of the load balancing server of a cloud computing system. The steady-state as well as the transient performance analyses have been carried out. Important measures of performance like average queue size, average delay, probability of task blocking, and the probability of no waiting in the queue are studied. Finally, some comparisons are performed which describe the effect of correlated task reneging over simple exponential reneging.

Multipath Routing in Elastic Optical Networks with Space-Division Multiplexing

This article introduces four multipath routing, core, and spectrum assignment algorithms, all of which employ image processing techniques for fast identification of allocable slots, since this is less computationally complex and adequate for real-time operation. Numerical results show that multipath routing can decrease the probability of blocking by one order of magnitude in relation to its single-path counterpart, but produce acceptable differential delay values.

Multiclass Energy Packet Networks with finite capacity energy queues

Energy Packet Network (EPN) consists of a queueing network formed by N blocks, where each of them is formed by one data queue, that handles the workload, and one energy queue, that handles packets of energy.We study an EPN model where the energy packets start the transfer. In this model, energy packets are sent to the data queue of the same block. An energy packet routes one workload packet to the next block if the data queue is not empty, and it is lost otherwise.We assume that the energy queues have a finite buffer size and if an energy packet arrives to the system when the buffer is full, jump-over blocking (JOB) is performed, and therefore with some probability it is sent to the data queue and it is lost otherwise.We first provide a value of the jump-over blocking probability such that the steady-state probability distribution of packets in the queues admits a product form solution. The product form is established for multiserver and multiclass data packet queues under FCFS, preemptive LCFS and PS discipline. Moreover, in the case of a directed tree queueing network, we show that the number of data packets in each subtree decreases as the JOB probability increases for each block.

Multi-objective evolutionary 3D face reconstruction based on improved encoder–decoder network

Three-dimensional (3D) face application has attracted significant attention the field of multimedia and image processing. However, the end-to-end 3D face reconstruction method is still immature, and there are some problems, such as overfitting caused by too few training sets and unacceptable 3D face texture alignment performance. Therefore, we design a novel approach to construct a 3D face, named multi-objective evolutionary 3D face reconstruction based on improved encoder–decoder network (MoEDN). This study introduces a regularization algorithm named feature map distortion (Disout); whose purpose is to strengthen the network generalization ability. Based on this, we construct a multi-objective evolutionary 3D face reconstruction model, in which decision variables are distortion probability, distorted block size, distorted intensity, probability step, and learning rate; and objective functions are loss and structural similarity (SSIM). We use four multi-objective evolutionary algorithms (NSGA-II, AGEII, NSLS, and MOEA/D) to optimize the proposed model. Experimental results demonstrate that NSLS has the best performance. In addition, compared with position map regression network (PRNet), 2D-assisted self-supervised learning (2DASL) and other state-of-the-art, the proposed model achieves better loss values and NME values. Therefore, the proposed multi-objective evolutionary 3D face reconstruction model has outstanding 3D facial reconstruction performance in large poses and face expression.