Path Computation Research Articles

Seismic Edge Detection by Application of Cepstral Decomposition to Data Driven Modeled Geologic Channel Feature in Niger Delta.

Seismic edge detection algorithm unmasks blurred discontinuity in an image and its efficiency is dependent on the precession of the processing scheme adopted. Data-driven modeling is a ast machine learning scheme and a formal usually automatic version of the empirical approach in existence for long time and which can be used in many different contexts. Here, a desired algorithm that can identify masked connection and correlation from a set of observations is built and used.Geologic models of hydrocarbon reservoirs facilitate enhanced visualization, volumetric calculation, well planning and prediction of migration path for fluid. In order to obtain new insights and test the mappability of a geologic feature, spectral decomposition techniques i.e. Discrete Fourier Transform (DFT), etc and Cepstral decomposition techniques, i.e Complex Cepstral Transform (CCT), etc can be employed. Cepstral decomposition is a new approach that extends the widely used process of spectral decomposition which is rigorous when analyzing very subtle stratigraphic plays and fractured reservoirs. This paper presents the results of the application of DFT and CCT to a two dimensional, 50Hz low impedance Channel sand model, representing typical geologic environment around a prospective hydrocarbon zone largely trapped in various types of channel structures. While the DFT represents the frequency and phase spectra of a signal, assumes stationarity and highlights the average properties of its dominant portion, assuming analytical, the CCT represents the quefrency and saphe cepstra of a signal in quefrency domain. The transform filters the field data recorded in time domain, and recovers lost sub-seismic geologic information in quefrency domain by separating source and transmission path effects. Our algorithm is based on fast Fourier transform (FFT) techniques and the programming code was written within Matlab software. It was developed from first principles and outside oil industry’s interpretational platform using standard processing routines. The results of the algorithm, when implemented on both commercial and general platforms, were comparable. The cepstral properties of the channel model indicate that cepstral attributes can be utilized as powerful tool in exploration problems to enhance visualization of small scale anomalies and obtain reliable estimates of wavelet and stratigraphic parameters. The practical relevance of this investigation is illustrated by means of sample results of spectral and cepstral attribute plots and pseudo-sections of phase and saphe constructed from the model data. The cepstral attributes reveal more details in terms of quefrency required for clearer imaging and better interpretation of subtle edges/discontinuities, sand–shale interbedding, differences in lithology. These positively impact on production as they serve as basis for the interpretation of similar geologic situations in field data.

REVERT: A Network Failure Recovery Method for Data Center Networks

As a repository that holds computing facilities, storage facilities, network facilities and other facilities, the Software Defined Data Center (SDDC) can provide computing and storage resources for users. For a SDDC, it is important to provide continuous services for users. Hence, in order to achieve high reliability in Software Defined Data Center Networks (SDDCNs), a network failure recovery method for software defined data center networks (REVERT) is proposed to recover failures in SDDCNs. In REVERT, the network failures that occurred in SDDCNs are classified into three types, which are switch failure, failure of links among switches and failure of links between switches and servers. Specially, except recovering the switch failure and failure of links between switches, REVERT can also recover the failures of links between the switches and servers. To achieve that, a failure preprocessing method used to classify the network failures, a data structure for storing and finding the affected flows, a server cluster agent for communicating with the server clustering algorithm and a routing path calculation method are designed in REVERT. Meanwhile, REVERT has been implemented and evaluated on RYU controller and Mininet using three routing algorithms. Compared with the link usage before recovering the network failures, when there are more than 200 flows in the network, the mean link usages only slightly increase at about 1.83 percent. More importantly, the evaluation results also demonstrate that except recovering switch failures, intra-topo link failures, REVERT has the ability of recovering failures of links between servers and edge switches successfully.

A novel path-based reproduction operator for multi-objective optimization

Abstract A large number of multi-objective evolutionary algorithms (MOEAs) have been proposed for the past two decades. However, few papers focus on the study of reproduction operator in MOEAs. In this work, we propose a novel path-based reproduction operator, termed path evolution (PE), to generate potential solutions more effectively for MOEAs. In PE, there is no mating selection, and the calculation of the evolution path is simple. Moreover, a new gene-sharing operation is proposed. The effectiveness of PE is validated by comparing it with three widely used reproduction operators and two state-of-the-art path-based reproduction operators. It is also reported that PE is very flexible to embed into different categories of MOEAs. The empirical results on three widely used test suites demonstrate the superiority, especially faster convergence ability, of PE.

Design and Assessment of FM-MCFs-Suited SDM-ROADMs With Versatile Spatial Group Configurations and Unified QoT Estimator

In this article, we focus on the design of different node architectures suitable for few-mode multi-core fibers (FM-MCFs) based networks. Both dimensions, core and mode, open different possible ways to group spatial channels depending on the physical impairments of the space-division multiplexed (SDM) optical fibers. Moreover, the channel switching across a group of cores/modes at once and the end-to-end routing are not only mandatory aspects for certain spatial channels, but also recommendable in order to reduce the node complexity/cost. Thus, we propose various SDM-capable node architectures based on versatile and homogeneous spatial group configurations. Then, a unified physical-layer-aware Quality of Transmission (QoT) estimator is formulated to not only evaluate these node architectures in a simulation tool, but also validate them in a real experimental environment using a stateful path computation element (PCE) as a central controller. The obtained results disclose that the cost-efficient node design parameter, namely, the size of the spatial group ${\boldsymbol{G}}$ , depends on both the network and traffic profile size. Specifically, for a national optical backbone network equipped with a homogeneous and hexagonally arranged 6-weakly-coupled modes and 7-weakly-coupled cores fibers, ${\boldsymbol{G }}$ equals 6, while for a continental backbone network, ${\boldsymbol{G}}$ can raise up to 14. In any case, we demonstrate that the cost-benefit tradeoff in node design must be analyzed in detail in order to meet the huge traffic volumes of the next years.

Application of Improved Dijkstra Algorithm in Coastal Tourism Route Planning

Chen, Y., 2020. Application of improved Dijkstra algorithm in coastal tourism route planning. In: Gong, D.; Zhang, M., and Liu, R. (eds.), Advances in Coastal Research: Engineering, Industry, Economy, and Sustainable Development. Journal of Coastal Research, Special Issue No. 106, pp. 251–254. Coconut Creek (Florida), ISSN 0749-0208.With the development of computer science and geographic information science, there are more and more applications for GIS. Shortest path analysis is a key issue in GIS geographic network analysis. One of the classical algorithms for calculating the shortest path is the Dijkstra algorithm, which is used in many projects to solve the shortest path problem. However, when the traditional Dijkstra algorithm solves the shortest path between nodes, a large number of nodes outside the identified nodes are calculated, which affects the speed of the algorithm. The Dijkstra algorithm is a classical algorithm for solving the shortest path in the embedded GIS system. By analyzing the Dijkstra algorithm, changing the storage structure, and examining graphing methods, the binary ordering tree algorithm based on the rectangular restricted area reduces the memory storage space. Shortening the query time requires optimizing the shortest path calculation process; the actual data test also shows the effectiveness of the algorithm.

Theory of quantum path computing with Fourier optics and future applications for quantum supremacy, neural networks and nonlinear Schr\xf6dinger equations

The scalability, error correction and practical problem solving are important challenges for quantum computing (QC) as more emphasized by quantum supremacy (QS) experiments. Quantum path computing (QPC), recently introduced for linear optic based QCs as an unconventional design, targets to obtain scalability and practical problem solving. It samples the intensity from the interference of exponentially increasing number of propagation paths obtained in multi-plane diffraction (MPD) of classical particle sources. QPC exploits MPD based quantum temporal correlations of the paths and freely entangled projections at different time instants, for the first time, with the classical light source and intensity measurement while not requiring photon interactions or single photon sources and receivers. In this article, photonic QPC is defined, theoretically modeled and numerically analyzed for arbitrary Fourier optical or quadratic phase set-ups while utilizing both Gaussian and Hermite-Gaussian source laser modes. Problem solving capabilities already including partial sum of Riemann theta functions are extended. Important future applications, implementation challenges and open issues such as universal computation and quantum circuit implementations determining the scope of QC capabilities are discussed. The applications include QS experiments reaching more than 2^{100} Feynman paths, quantum neuron implementations and solutions of nonlinear Schrödinger equation.

SDN-Enabled Energy-Efficient Routing Optimization Framework for Industrial Internet of Things

The traditional Internet architecture relies on the best-effort principle, which is not suitable for critical industrial Internet of Things (IIoT) applications such as healthcare systems with stringent quality-of-service (QoS) requirements. In this article, a software-defined network (SDN) based on an analytical parallel routing framework is proposed by using the massive processing power of a graphics processing unit (GPU) for dynamically optimizing multiconstrained QoS parameters in the IIoT. The framework considers three types of QoS applications for smart healthcare traffic: loss-sensitive, delay-sensitive, and jitter-sensitive. A QoS-enabled routing optimization problem is formulated as a max-flow min-cost problem, while a greedy heuristic that dispatches the path calculation task concurrently to the GPU for calculating optimal forwarding paths considering the QoS requirement of each flow is proposed. The results show that the proposed scheme efficiently utilizes the limited bandwidth cost in terms of energy and bandwidth while satisfying the QoS requirement of each flow with maximizing the network resources for future IIoT traffic flows. Comparative analysis of simulation results with shortest path delay, Lagrangian relaxation-based aggregated cost, and Sway schemes indicate a reduced violation in the service-level agreement by 17%, 19%, and 4%, respectively, by using the AttMpls topology, while it is 48%, 44%, and 7% when the Goodnet topology is used. Moreover, SEQOS is seen to be energy efficient and eight times faster than the benchmark algorithms in large IIoT networks.

Pilgrim: A thermal rate constant calculator and a chemical kinetics simulator

Pilgrim is a program written in Python and designed to use direct dynamics in the calculation of thermal rate constants of chemical reactions by the variational transition state theory (VTST), based on electronic structure calculations for the potential energy surface. Pilgrim can also simulate reaction mechanisms using kinetic Monte Carlo (KMC).For reaction processes with many elementary steps, the rate constant of each of these steps can be calculated by means of conventional transition state theory (TST) or by using VTST. In the current version, Pilgrim can evaluate thermal rates using the canonical version of reaction-path VTST, which requires the calculation of the minimum energy path (MEP) associated with each elementary step or transition structure. Multi-dimensional quantum effects can be incorporated through the small-curvature tunneling (SCT) approximation. These methodologies are available both for reactions involving a single structure of the reactants and the transition state and also for reactions involving flexible molecules with multiple conformations of the reactant and/or of the transition state. For systems with many conformers, the program can evaluate each of the elementary reaction rate constants by multipath canonical VTST or multi-structural VTST. Moreover, the reactant can be unimolecular or bimolecular.Torsional anharmonicity can be incorporated through either the MSTor or the Q2DTor programs. Dual-level calculations are also available in Pilgrim: automatic high-level single-point energies can be used to correct the energy of reactants, transition states, products, and MEP points using the interpolated single-point energies (ISPE) algorithm.When the rate constants of all the chemical processes of interest are known, by means of their calculation using Pilgrim or alternatively through analytical fits to the rate constants as functions of temperature, it is possible to simulate a multistep mechanism under specified laboratory conditions using KMC. This algorithm allows performing a kinetic simulation to monitor the evolution of each chemical species with time and obtain the product yields. Program summaryProgram Title: PilgrimCPC Library link to program files:http://dx.doi.org/10.17632/24cj4dwxvg.1Developer’s repository link:https://github.com/cathedralpkg/pilgrim/releases; https://comp.chem.umn.edu/pilgrim; https://conservancy.umn.edu/handle/11299/166578Licensing provisions: MITProgramming language: Python 3Nature of problem: Calculation of thermal rate constants for bimolecular and unimolecular chemical reactions and simulation of reaction mechanismsSolution method: The program uses variational transition state theory to calculate thermal rate constants and kinetic Monte Carlo to simulate reaction mechanisms.Restrictions and unusual features: The program cannot treat reactions without saddle points. Unimolecular reactions are calculated only in the high-pressure limit. Direct dynamics calculations with Pilgrim require an electronic structure package to be supplied by the user; currently, Pilgrim supports the Gaussian [Frisch et al. (2003), Frisch et al. (2016a), Frisch et al. (2016b)] and Orca [Neese (2011)] electronic structure packages. Pilgrim has an especially powerful suite of options for handling torsional anharmonicity and multistructural effects.

EnFlow: An Energy-Efficient Fast Flow Forwarding Scheme for Software-Defined Networks

In recent years, the huge expansion of Datacenters (DC) to execute billions of end-user applications in real-time leads to a large amount of energy consumption across the globe. So, the traditional TCP/IP-based networks which are being used for DC inter-connections are facing challenges of managing stringent Quality-of-Service (QoS) requirements of different applications of the end-users and service providers. Moreover, the existing solutions rely on distributed architecture and do not scale for large scale data centers. The issue of high power consumption at DC arises with the increase in the number of nodes and links in the network. Also, it becomes problematic on the DC whenever the underlying network resources (switches and routers) are not efficiently utilized at the time of peak data traffic resulting in high operational cost of energy utilization. However, Software-Defined Networking (SDN) emerges as one of the leading technologies to address the aforementioned issues using the programmable switches and controllers. Inspired from these facts, in this paper, we have formulated the Energy-Aware Routing (EAR) problem of DCs as a Mixed Integer Non-Linear Programming (MINLP) for which an Energy-Efficient Fast Flow Forwarding (EnFlow) scheme is designed. The EnFlow scheme uses the power-saving mode of the network to solve the EAR problem. It has three modules namely- priority scheduling, routing, and re-routing. The first module works according to the First-in-First-Out Push Out Priority (FIFO-POP) scheduling using the multiple OpenFlow switches. The FIFO-POP is designed to save the energy usage of multiple switches by reducing the average waiting time of incoming packets in the queue buffers. The second module is based upon an efficient flow re-routing for a new node and link adaptation to provide the maximum bandwidth to the wired links. The third module is based upon the meta-heuristic Ant Colony Routing (ACR) to execute the stochastic decision policy on the network controller for computation of the shortest path of the forwarding nodes. The proposed EnFlow scheme is simulated using the data traces of 34 cities of NorthAmerica zone with Omnet++ 5.1 using various performance evaluation metrics. The results obtained demonstrated that the proposed EnFlow scheme is 24.55% and 71.15% more energy-efficient in comparison to the RE-FPR and ILP-EAR schemes. Also, it consumes 9.72% and 40.83% lower energy in comparison to the FFHA and EXR schemes respectively.

Prediction of Node and Link Failures in Mobile Ad Hoc Network Using Hello Based Path Recovery Routing Protocol

Mobile ad hoc network is composed of wire free nodes that are moving in nature and they are configured to form a self-configured infrastructure less network. So, the energy consumed by every node is varied due to the movement of the nodes. Open Shortest Path First is the shortest path estimating routing scheme which is created more energy and more delay for data transmission when the link fails in the network. To overcome this difficulty, this paper proposed an efficient innovative hello based path recovery (HBPR) routing protocol for the shortest path calculation. During transmission, if the link fails in the network layer then the HBPR scheme creates the alternate path, thus it reduced the delay time and energy consumption. Subsequently, the novel simplified honey pot optimization (SHPO) is introduced to predict the harmful nodes within the network. This SHPO maintains the path stability and node security of the network also it will improve the quality of service parameters. The implementation of this research is done by MATLAB R2018b and the simulation results illustrate the performance of the proposed algorithm. This approach achieves better results in packet delivery ratio, delay, average energy consumption, and throughput compared to other existing approaches.

A novel reduced i-propanol-n-butanol-ethanol (IBE)/diesel mechanism for engine combustion and emissions prediction

The direct application of i-propanol-n-butanol-ethanol (IBE) as an assuring alternative biofuel of internal combustion engines to get rid of the high recovery cost and extra energy consumption associated with n-butanol production has been gaining popularity, the research on performance and emissions of engines fueled with IBE are starting to be carried out. However, the available chemical reaction mechanism of IBE for engine combustion model is rarely reported. Therefore, a reduced mechanism named HT-IBE including 151 species and 775 reactions to track the combustion and emissions behavior of engine fueled with IBE/diesel blends was proposed in the present research. It was first meticulously developed based on the coupling of the reduced mechanisms of i-propanol, n-butanol, ethanol and diesel surrogates and the simplified NOx and soot formation models using combined reduction methods of directed relation graph with error propagation and sensitivity analysis (DRGEPSA), computational singular perturbation (CSP) and reaction path analysis (RPA) methods. To improve the predictive power of the mechanism, the sensitive reactions to ignition delay were identified and optimized based on sensitivity analysis. The mechanism was validated against ignition delay, laminar flame speed, and species mole fractions over a wide range of engine relevant conditions. The 3D engine simulation was further carried out using KIVA-3V2-CANTERA coupled with the mechanism to simulate the in-cylinder pressure, heat release rate, NOx and the soot emissions. Results show that the HT-IBE mechanism can reliably predict the combustion characteristics and emissions characteristics of IBE/diesel blends in the CI engine.

SHAREK*: A Scalable Matching Method for Dynamic Ride Sharing

Due to its significant economic and environmental impact, sharing the ride among a number of drivers (i.e., car pooling) has recently gained significant interest from industry and academia. Hence, a number of ride sharing services have appeared along with various algorithms on how to match a rider request to a driver who can provide the ride sharing service. However, existing techniques have several limitations that affect the quality of the ride sharing service, and hence hinder its wide applicability. This paper proposes SHAREK*; a scalable and efficient ride sharing service that overcomes the limitations of existing approaches. SHAREK* allows riders requesting the ride sharing service to indicate the maximum price they are willing to pay for the service, the maximum waiting time before being picked up and the maximum arrival time for arriving the destination. In the mean time, SHAREK* computes the price of the service based on the distance of the rider trip and the detour that the driver will make to offer the service. Then, SHAREK* returns a set of drivers that can make it to the rider within its price and temporal constraints. Since there could be many of such drivers, SHAREK* internally prunes those drivers that are dominated by others, i.e., they provide higher price and higher waiting time (or arrival time) than other drivers. To realize its efficiency and scalability, SHAREK* employs a set of early pruning techniques that minimize the need for any actual shortest path computations.

A hybrid technique for single-source shortest path-based on A* algorithm and ant colony optimization

<span lang="EN-US">In the single-source shortest path (SSSP) problem, the shortest paths from a source vertex v to all other vertices in a graph should be executed in the best way. A common algorithm to solve the (SSSP) is the A* and Ant colony optimization (ACO). However, the traditional A* is fast but not accurate because it doesn’t calculate all node's distance of the graph. Moreover, it is slow in path computation. In this paper, we propose a new technique that consists of a hybridizing of A* algorithm and ant colony optimization (ACO). This solution depends on applying the optimization on the best path. For justification, the proposed algorithm has been applied to the parking system as a case study to validate the proposed algorithm performance. First, A*algorithm generates the shortest path in fast time processing. ACO will optimize this path and output the best path. The result showed that the proposed solution provides an average decreasing time performance is 13.5%.</span>

SLEEF: A Portable Vectorized Library of C Standard Mathematical Functions

In this paper, we present techniques used to implement our portable vectorized library of C standard mathematical functions written entirely in C language. In order to make the library portable while maintaining good performance, intrinsic functions of vector extensions are abstracted by inline functions or preprocessor macros. We implemented the functions so that they can use sub-features of vector extensions such as fused multiply-add, mask registers and extraction of mantissa. In order to make computation with SIMD instructions efficient, the library only uses a small number of conditional branches, and all the computation paths are vectorized. We devised a variation of the Payne-Hanek argument reduction for trigonometric functions and a floating point remainder, both of which are suitable for vector computation. We compare the performance of our library to Intel SVML.

Real-time exact contour error calculation of NURBS tool path for contour control

The requirement of high-speed and high-precision contouring with NURBS tool path to the CNC machining process is increasing. The calculation of the contouring error (CE) of the NURBS tool path to real time is necessary for the advanced contour control like the cross-coupled controller (CCC). Based on the complexity of the CE calculation of NURBS, most studies use estimating methods to approximate the real CE in the controller. However, when the cutter is at the large curvature zones in the high-speed machining, the difference between the estimated the CE and the real one becomes large. To solve this problem, an algorithm to calculate the exact CE of NURBS tool path to real time is proposed. The calculation model of the CE of NURBS is derived. Also, the numerical calculation algorithm from Brent’s method is given to solve the CE model. Multiple solutions of CE model is discussed and real-time solution tracking method is developed to determine the intervals of the candidate solutions for numerical calculation. Contour error for control (CCE) is proposed for controller design to improve the stability of contour control, which is different from the definition of conventional control error for measurement (MCE). The proposed algorithm is validated for both the simulation and the experiment on the X–Y table. Experimental results show the effectiveness of the proposed algorithm and the actual CE is reduced significantly compared with other methods.

Two-Phase Virtual Network Function Selection and Chaining Algorithm Based on Deep Learning in SDN/NFV-Enabled Networks

With the advances of Software-Defined Networks (SDN) and Network Function Virtualization (NFV), Service Function Chain (SFC) has been becoming a popular paradigm to carry and complete network services. Such new computing and networking paradigm enables Virtual Network Functions (VNFs) to be placed in software entities/virtual machines over a network of physical equipments in elastic and flexible way with low capital and operation expenses. VNFs are chained together to steer traffic as needed. However, most of the existing traffic steering and routing path computation algorithms for SFC are complex, unscalable, and low time-efficiency. In this paper, we study the VNF Selection and Chaining Problem (VNF-SCP) in SDN/NFV-enabled networks. We formulate VNF-SCP as a Binary Integer Programming (BIP) model in order to compute routing path for each SFC Request (SFCR) with the minimum end-to-end delay. Then, a novel Deep Learning-based Two-Phase Algorithm (DL-TPA) is introduced, where VNF selection network and VNF chaining network are designed to achieve intelligent and efficient VNF selection and chaining for SFCRs. Performance evaluation shows that DL-TPA can achieve high prediction accuracy and time efficiency of routing path computation, and the overall network performance can be improved significantly.

Mesh-based tool path calculations for tubular joints

Manufacturing of large steel tube structures is faced with excessive welding, fit-up and rework times in tube joints, due to various types of deviation from nominal shape of the tubes. This article presents a procedure and geometry calculus for generating cutting and welding paths based on measured geometries. The procedure poses the two measured meshes as per construction specification and invokes a mesh intersection procedure to get the mesh intersection path; performs an optional smoothing; interpolates the smoothed path to a specified angular resolution; estimates the two surface normal vectors and the two surface tangents in the plane spanned by the normals at each interpolation point; calculates the cutting tool and welding tool approach directions for obtaining the specified welding groove geometry at each interpolation point; and finally stores all the data parameterized by the interpolation angle. Illustrations of results with both synthetic, representative meshes and meshes obtained from scanning of actual tubes at the shop-floor at a manufacturer are presented. The reference implementation for the developed software tool is based on Python and uses the mesh modeller from the 3D creation suite Blender as platform.

Efficient optimization method for finding minimum energy paths of magnetic transitions

Efficient algorithms for the calculation of minimum energy paths of magnetic transitions are implemented within the geodesic nudged elastic band (GNEB) approach. While an objective function is not available for GNEB and a traditional line search can, therefore, not be performed, the use of limited memory Broyden–Fletcher–Goldfarb–Shanno (LBFGS) and conjugate gradient algorithms in conjunction with orthogonal spin optimization (OSO) approach is shown to greatly outperform the previously used velocity projection and dissipative Landau–Lifschitz dynamics optimization methods. The implementation makes use of energy weighted springs for the distribution of the discretization points along the path and this is found to improve performance significantly. The various methods are applied to several test problems using a Heisenberg-type Hamiltonian, extended in some cases to include Dzyaloshinskii–Moriya and exchange interactions beyond nearest neighbours. Minimum energy paths are found for magnetization reversals in a nano-island, collapse of skyrmions in two-dimensional layers and annihilation of a chiral bobber near the surface of a three-dimensional magnet. The LBFGS-OSO method is found to outperform the dynamics based approaches by up to a factor of 8 in some cases.

FPGA Implementation of Homotopic Path Planning Method with Automatic Assignment of Repulsion Parameter

In recent times, autonomous robots have become more relevant, aiming not only to be an extension of mobility and human performance but also allowing them to independently solve specific problems such as finding free-collision paths within some defined environments. In order to achieve this, several techniques have been developed, like action-reaction algorithms, sampling-based algorithms, and deterministic algorithms such as the Homotopy Path Planning Method (HPPM). This work presents, for the first time, a complete deterministic collision-free path planning scheme implemented in FPGA, which is mounted on a Scribbler 2 robot from Parallax. Then, an automatic algorithm of the repulsion parameter for the HPPM method is presented, using as a reference the minimum distance between the center of each obstacle with respect to the homotopic ideal path; furthermore, an algorithm is proposed for discriminating dead-end routes and collision risk trajectories, which allows us to obtain a feasible free-collision path that takes into account the robot dimensions. Besides, comparative performance tests have been carried out against other path-finding methods from the low degrees of freedom (low DoF) and sampling-based planners. Our proposal exhibits path calculation times which are 5 to 10 times faster on FPGA implementation, compared to the other methods and 10 to 100 times faster on PC implementation also compared to the rest. Similar results are obtained with regards to memory consumption, namely 20 to 200 times lower on FPGA implementation and 10 to 100 times lower on PC implementation.

Traffic Engineering With Three-Segments Routing

Segment Routing (SR) is a new fertile ground for Traffic Engineering (TE). By decomposing forwarding paths into segments, which specify a list of intermediate delivery points that a packet must visit on its way to the final destination, SR improves TE tasks and enables new solutions for the optimization of network resource utilization. This work proposes an Evolutionary Computation approach that enables Path Computation Element (PCE), or Software-defined Network (SDN) controllers, to optimize SR configurations for improved traffic distribution. Furthermore, we present a robust semi-oblivious method to address the variability of traffic requirements as well as alternative approaches to ensure a good network performance after link failures. In all cases, the optimization of network resource utilization is achieved using at the most three segments to configure each SR path. Moreover, all proposed optimization methods are made publicly available in a optimization framework developed by the authors.