Computing Grids Research Articles

Model for Anticipating Frank Failures in Computing Grids

Computing grids are infrastructures that provide almost infinite computing capacities, they are now used in all fields, from the study of pandemics to the monitoring of rocket trajectories and the study of meteorological and climatic phenomena. They have a distributed and heterogeneous architecture that gives them unlimited computing performance. They are made up of several computing nodes that are subject to failures like frank failures. A frank failure in a computing grid is an abnormal and unexpected interruption of a node. Many frank failure tolerance protocols have been proposed in the literature but none of these protocols integrate the anticipation of frank failures. The objective of this article is to propose a model based on the PDEVS formalism of frank failure tolerance in a computing grid that allows the anticipation of frank failures. The proposed model relies on the temperature variation of electronic components and on the state of the hard disk through the values provided by SMART data to predict a probable frank failure of a node. The results of the simulations on the different scenarios that we have carried out show that our results provide better performances than those proposed in the literature when the number of nodes to tolerate is greater than 200.

A new approach to analyze the equilibrium and transient behaviors of particulate systems and the subsequent application to multiphase fluid systems

Fast and robust computation for population balance models is a crucial requirement for simulating particulate systems. Despite all the recent advances in developing relevant computational algorithms, either efficiency or robustness can only be achieved at the expense of the other. However, optimal compromise is possible by having prior estimates of the system's internal length and time scales. Thus, an approximation technique is introduced in this study to extract equilibrium and transient behaviors for a particulate system considering coalescence and breakage phenomena. The approximation method is developed by simplifying assumptions on the available analytical solution for a spatially homogeneous population balance equation with simple kernels. The derived equilibrium and transient equations suggest that the system is governed by a dimensionless group that can describe the equilibrium distribution of the system as well as the rate and the direction that the system is likely to evolve. The method is applied to two different sets of complex breakage and coalescence kernels used for liquid-liquid dispersed systems. The approximated time and length scales were validated with numerical results; thus, the approximation can be used to generate targeted element-based orthogonal collocation grids for fast and robust computation of transient and steady-state particulate systems. This approach can significantly decrease the computation time, typically by 40–70 % for steady-state conditions.

Examining the cost-effectiveness of electric vehicle policy in India

ABSTRACT Deployment of electric vehicles (EVs) is being widely used by governments across the world as a policy tool to mitigate the catastrophic effects of global warming and climate change. However, the demand for EVs has been slow in many large automotive markets, including India. To increase the uptake of EVs in India, the central government has introduced a new consumer incentive program and a special tax rate for EVs. In this paper, we estimate the cost-effectiveness of these EV promotion programs in achieving CO2 emission reduction for each State in India. We use emission data of the chosen vehicle and CO2 emission intensity data of each State’s power grid for computation. The results are then compared to other benchmark investments in climate change mitigation. The findings indicate that subsidizing EVs may not be the most cost-efficient method to achieve CO2 reduction.

A job scheduling algorithm based on parallel workload prediction on computational grid

Generally, the computational grid consists of a large number of computing nodes, some of them are idle due to the uneven geographical distribution of computing requirements. This may cause workload unbalancing problems, which affect the performance of large-scale computational grids. In order to balance the computing requirements and computing nodes, we propose a job scheduling algorithm based on the workload prediction of computing nodes. We first analyze the causes of workload imbalance and the feasibility of reallocating computing resources. Secondly, we design an application and workload-aware scheduling algorithm (AWAS) by combining the previously designed workload prediction model. To reduce the complexity of the AWAS algorithm, we propose a parallel job scheduling method based on computing node workload prediction. The experiments show that the AWAS algorithm can balance the workload among different computing nodes on the real-world dataset. In addition, we propose the parallelism of workload prediction model from the perspective of internal structure and data set to make AWAS apply to more computing nodes of the large-scale computing grids. Experimental results show that the combination of the two can achieve satisfactory acceleration efficiency.

Preconditioned central moment lattice Boltzmann method on a rectangular lattice grid for accelerated computations of inhomogeneous flows

Convergence acceleration of flow simulations to their steady states at lower Mach numbers can be achieved via preconditioning the lattice Boltzmann (LB) schemes that alleviate the associated numerical stiffness, which have so far been constructed on square lattices. We present a new central moment LB method on rectangular lattice grids for efficient computations of inhomogeneous and anisotropic flows by solving the preconditioned Navier–Stokes (PNS) equations. Moment equilibria corrections are derived via a Chapman–Enskog analysis for eliminating the truncation errors due to grid-anisotropy arising from the use of the rectangular lattice and the non-Galilean invariant cubic velocity errors resulting from an aliasing effect on the standard D2Q9 lattice for consistently recovering the PNS equations. Such corrections depend on the diagonal components of the velocity gradients, which are locally obtained from the second order non-equilibrium moments and parameterized by an associated grid aspect ratio r and a preconditioning parameter γ, and the speed of sound in the collision model is naturally adapted to r via a physically consistent strategy. We develop our approach by using a robust non-orthogonal moment basis and the central moment equilibria are based on a matching principle, leading to simpler expressions for the corrections for using the rectangular grids and for representing the viscosities as functions of the relaxation parameters, r and γ, and its implementation is modular allowing a ready extension of the existing LB schemes based on the square lattice. Numerical simulations of inhomogeneous and anisotropic shear-driven bounded flows using the preconditioned rectangular central moment LB method demonstrate the accuracy and significant reductions in the numbers of steps to reach the steady states for various sets of characteristic parameters.

Authenticating Devices in Fog-mobile Edge Computing Environments through a Wireless Grid Resource Sharing Protocol

The rapid growth of the Internet of Things (IoT), cloud computing, Fog computing, mobile edge computing and wireless grids has resulted in the widespread deployment of relatively immature technology. These technologies, which will primarily use 5G wireless communication networks, are becoming popular because they can be deployed quickly with little infrastructure and lends themselves to environments utilizing numerous internet connected devices (ICD). There are, however, many significant challenges faced by security designers, engineers and implementers of these networks in ensuring that the level of security afforded is appropriate. Because of the threat of exploitation, these networks have to be protected by a robust security architecture due to these technologies being plagued with security problems. The authentication of smart ICDs to IoT networks is a critical mechanism for achieving security on these new information system platforms. This article identifies an authentication process required for these ICDs, which will need to prove their identity to authenticate to an IoT fog-mobile edge computing (FMEC) cloud network through a wireless grid authentication process. The purpose of this article is to begin to hypothesize a generic authentication methodology for these FMEC clouds uses in an IoT architecture. The proposed methodology, called wg-IoT, must include the integration of Fog computing, wireless grids and mobile edge computing clouds to create this new IoT architecture. An authentication process developed from the resource sharing protocol (RSP) from a wireless grid is first developed and proposed for the authentication of ICDs. The wireless grid core components must be embedded in IoT devices or sensors depending on their capability to handle five primary functions: management of identification [ID] and presence, permissions management, data transferability, application-programming interface [API] and security.

Fog Computing for Realizing Smart Neighborhoods in Smart Grids

Cloud Computing provides on-demand computing services like software, networking, storage, analytics, and intelligence over the Internet (“the cloud”). But it is facing challenges because of the explosion of the Internet of Things (IoT) devices and the volume, variety, veracity and velocity of the data generated by these devices. There is a need for ultra-low latency, reliable service along with security and privacy. Fog Computing is a promising solution to overcome these challenges. The originality, scope and novelty of this paper is the definition and formulation of the problem of smart neighborhoods in context of smart grids. This is achieved through an extensive literature study, firstly on Fog Computing and its foundation technologies, its applications and the literature review of Fog Computing research in various application domains. Thereafter, we introduce smart grid and community MicroGrid concepts and, their challenges to give the in depth background of the problem and hence, formalize the problem. The smart grid, which ensures reliable, secure, and cost-effective power supply to the smart neighborhoods, effectively needs Fog Computing architecture to achieve its purpose. This paper also identifies, without rigorous analysis, potential solutions to address the problem of smart neighborhoods. The challenges in the integration of Fog Computing and smart grids are also discussed.

Forecasting network throughput of remote data access in computing grids

Computing grids are key enablers of computational science. Researchers from many fields (High Energy Physics, Bioinformatics, Climatology, etc.) employ grids for execution of distributed computational jobs. These computing workloads are typically data-intensive. The current state of the art approach for data access in grids is data placement: a job is scheduled to run at a specific data center, and its execution commences only once the complete input data has been transferred there. An alternative approach is remote data access: a job may stream the input data directly from arbitrary storage elements. Remote data access brings two innovative benefits: (1) the jobs can be executed asynchronously with respect to the data transfer; (2) when combined with data placement on the policy level, it can aid in the optimization of the network load, since these two data access methodologies partially exhibit nonoverlapping bottlenecks. However, in order to employ this technique systematically, the properties of its network throughput need to be studied carefully. This paper presents experimentally identified parameters of remote data access throughput, statistically tested formalization of these parameters and a derived throughput forecasting model. The model is applicable to large computing workloads, robust with respect to arbitrary dynamic changes in the grid infrastructure and exhibits a long-term prediction horizon. Its purpose is to assist various stakeholders of the grid in decision-making related to data access patterns. This work is based on measurements taken on the Worldwide LHC Computing Grid at CERN.

An approach to calculating large strain accumulation for discrete element simulations of granular media

SummaryFor research on granular materials, establishing a method to calculate continuum strain from particle displacements is necessary for understanding the material behaviour at macro‐level and developing continuum constitutive models. Existing methods are generally based on constructing a mesh or background grid to calculate strain from particle motions. These methods offer rigorous ways to measure strain for granular materials; however, they suffer from several problems such as mesh distortion and lacking grid‐to‐particle strain mapping procedure, which hinders their capability of calculating strain accumulation during large deformation processes of granular media. To address this issue, this study proposes a new strain calculation method for discrete element simulations of granular materials. This method describes a particle assembly as an equivalent continuum system of material points, each of which corresponds to a particle centre and represents a continuous region with its initial volume/area presumably equal to the volume/area of Voronoi cells generated in accordance with the particle assembly configuration. Smooth Particle Hydrodynamics (SPH) interpolation functions are then employed to calculate strain for these material points. This SPH‐based method does not require any mesh or background grid for computation, leading to advantages in calculating strain accumulation under large deformation. Simulations of granular materials in both uniform and heterogeneous gradations were carried out, and strain results obtained by the proposed method indicate good agreements with analytical and numerical solutions. This demonstrates its potential for strain calculations in discrete element simulations of granular materials involving large deformations and/or large displacements.

Development and analysis of anatomically real breast phantoms using different dispersion models

An effective method to develop anatomically real numerical breast phantoms for T1-weighted MR images of different tissue densities is presented. The dielectric properties for breast tissues are calculated and analyzed using different dispersion models (i.e., one- and two-pole cole–cole and Debye models). The method presented in this paper propounds significant improvements in comparison with existing MRI-based numerical phantoms in terms of denoising of images, tissue segmentation, nonlinear mapping of dielectric properties with realistic shapes using all the dispersion models and densitywise classification of phantoms. This method is a multistep approach in which each MRI voxel is mapped with the appropriate dielectric properties according to different dispersion models. The MRI data was collected and interpolated according to the size of the uniform grid for finite difference time domain computations followed by the preprocessing of MR images to enhance them. Thereafter, the voxel intensities were segregated into two groups as adipose and fibro glandular tissues. These tissue intensities were assigned the corresponding dielectric properties. Three-dimensional (3-D) numerical phantoms were created according to all the dispersion models. After the comparison among the models, it has been found that, along with frequency, the dielectric properties vary according to the variation of the dispersion model parameters. It was also observed that the dielectric properties calculated from one-pole cole–cole and two-pole Debye models are more close to the real properties of breast tissues than other models. A generalized method has been defined for developing the 3-D phantoms for all classes of breast according to the inhomogeneity of fibroglandular tissues using the dispersion models. The frequency-dependent and dispersion model parameters-dependent dielectric properties have been assigned to the phantoms. These real-like phantoms after 3-D printing would help researchers working in the field of breast cancer detection studies.

Animation of crack propagation by means of an extended multi-body solver for the material point method

We propose a multi-body solver that extends the Material Point Method (MPM) to simulate cracks in computer animation. We define cracks as the intersection between pieces of bodies created by a pre-fracture process and held together by massless particle constraints (glue particles). These pieces are simulated using a MPM multi-body solver extended by us to efficiently handle N-body collisions. Benefits of the present work include (1) low computational overhead compared to a normal MPM algorithm; (2) good scaling in three dimensions due to our use of sparse grids for background computations; (3) allowing for an easy and controllable setup phase to simulate a desired material failure mode, which is especially useful for computer animation.

Analytical integration of 0th, 2nd, and 4th order polynomial filtering functions on unstructured grid for dispersed phase fraction computation in an Euler–Lagrange approach

This paper presents an analytical approach to evaluate the volume integrals emerging during dispersed phase fraction computation in Lagrangian–Eulerian methods. It studies a zeroth, second, and fourth order polynomial filtering function in test cases featuring structured and unstructured grids. The analytical integration is enabled in three steps. First, the divergence theorem is applied to transform the volume integral into surface integrals over the volumes’ boundaries. Secondly, the surfaces are projected alongside the first divergence direction. Lastly, the divergence theorem is applied for the second time to transform the surface integrals into line integrals. We propose a generic strategy and simplifications to derive an analytical description of the complex geometrical entities such as non-planar surfaces. This strategy enables a closed solution to the line integrals for polynomial filtering functions. Furthermore, this paper shows that the proposed approach is suitable to handle unstructured grids. A sine wave and Gaussian filtering function is tested and the fourth order polynomial is found to be a good surrogate for the sine wave filtering function as no expensive trigonometric evaluations are necessary.

ComBos: A complete simulator of Volunteer Computing and Desktop Grids

Volunteer Computing is a type of distributed computing in which ordinary people donate their idle computer time to science projects like SETI@Home, Climateprediction.net and many others. In a similar way, Desktop Grid Computing is a form of distributed computing in which an organization uses its existing computers to handle its own long-running computational tasks. BOINC is the main middleware that provides a software platform for Volunteer Computing and desktop grid computing, and it became generalized as a platform for distributed applications in areas as diverse as mathematics, medicine, molecular biology, climatology, environmental science, and astrophysics. In this paper we present a complete simulator of BOINC infrastructures, called ComBoS. Although there are other BOINC simulators, none of them allow us to simulate the complete infrastructure of BOINC. Our goal was to create a complete simulator that, unlike the existing ones, could simulate realistic scenarios taking into account the whole BOINC infrastructure, that other simulators do not consider: projects, servers, network, redundant computing, scheduling, and volunteer nodes. The outputs of the simulations allow us to analyze a wide range of statistical results, such as the throughput of each project, the number of jobs executed by the clients, the total credit granted and the average occupation of the BOINC servers. The paper describes the design of ComBoS and the results of the validation performed. This validation compares the results obtained in ComBoS with the real ones of three different BOINC projects (Einstein@Home, SETI@Home and LHC@Home). Besides, we analyze the performance of the simulator in terms of memory usage and execution time. The paper also shows that our simulator can guide the design of BOINC projects, describing some case studies using ComBoS that could help designers verify the feasibility of BOINC projects.

Face recognition using elastic grid matching through photoshop: A new approach

Abstract: Computing grids propose to be a very efficacious, economic and ascendable way of image identification. In this paper, we propose a grid based face recognition overture employing a general template matching method to solve the timeconsuming face recognition problem. A new approach has been employed in which the grid was prepared for a specific individual over his photograph using Adobe Photoshop CS5 software. The background was later removed and the grid prepared by merging layers was used as a template for image matching or comparison. This overture is computationally efficient, has high recognition rates and is able to identify a person with minimal efforts and in short time even from photographs taken at different magnifications and from different distances.

Asynchronous replica exchange software for grid and heterogeneous computing

Parallel replica exchange sampling is an extended ensemble technique often used to accelerate the exploration of the conformational ensemble of atomistic molecular simulations of chemical systems. Inter-process communication and coordination requirements have historically discouraged the deployment of replica exchange on distributed and heterogeneous resources. Here we describe the architecture of a software (named ASyncRE) for performing asynchronous replica exchange molecular simulations on volunteered computing grids and heterogeneous high performance clusters. The asynchronous replica exchange algorithm on which the software is based avoids centralized synchronization steps and the need for direct communication between remote processes. It allows molecular dynamics threads to progress at different rates and enables parameter exchanges among arbitrary sets of replicas independently from other replicas. ASyncRE is written in Python following a modular design conducive to extensions to various replica exchange schemes and molecular dynamics engines. Applications of the software for the modeling of association equilibria of supramolecular and macromolecular complexes on BOINC campus computational grids and on the CPU/MIC heterogeneous hardware of the XSEDE Stampede supercomputer are illustrated. They show the ability of ASyncRE to utilize large grids of desktop computers running the Windows, MacOS, and/or Linux operating systems as well as collections of high performance heterogeneous hardware devices.

Large Scale Explorative Oligonucleotide Probe Selection for Thousands of Genetic Groups on a Computing Grid: Application to Phylogenetic Probe Design Using a Curated Small Subunit Ribosomal RNA Gene Database

Phylogenetic Oligonucleotide Arrays (POAs) were recently adapted for studying the huge microbial communities in a flexible and easy-to-use way. POA coupled with the use of explorative probes to detect the unknown part is now one of the most powerful approaches for a better understanding of microbial community functioning. However, the selection of probes remains a very difficult task. The rapid growth of environmental databases has led to an exponential increase of data to be managed for an efficient design. Consequently, the use of high performance computing facilities is mandatory. In this paper, we present an efficient parallelization method to select known and explorative oligonucleotide probes at large scale using computing grids. We implemented a software that generates and monitors thousands of jobs over the European Computing Grid Infrastructure (EGI). We also developed a new algorithm for the construction of a high-quality curated phylogenetic database to avoid erroneous design due to bad sequence affiliation. We present here the performance and statistics of our method on real biological datasets based on a phylogenetic prokaryotic database at the genus level and a complete design of about 20,000 probes for 2,069 genera of prokaryotes.

F091005 数値解析による流体騒音予測の現状と今後の課題([F09100]環境工学企画,数値空力音響の最前線と課題)

This presentation describes the present status and future perspectives of the computational aeroacoustics, by showing several applications to flow-related industrial products. In particular, it focuses on the sound generated in relatively low-speed flows. Aerodynamic sound originates from the fluctuations in the fluid flows, and therefore, it can be predicted by combined computations of fluid-flow and sound propagation, provided that the source fluctuations in the fluid flows can be accurately predicted. Due to the remarkable speed up of the computer performance, the affordable number of the grids for fluid-flow computations has already reached to several tens billion, and will probably reach several hundred billion within five years. This number of the computational grids allows us to accurately predict fluid motion in micro-meter scales, which contributes to the generation of sound in frequency range of several thousand hertz. Therefore, it is almost certain that we will be able to predict, with a sufficient accuracy, source fluctuations in the fluid flows, and hence, the method for predicting sound propagation will become more important.

A Multicriterion Fuzzy Classification Method with Greedy Attribute Selection for Anomaly-based Intrusion Detection

Intrusion is widely recognized as a chronic and recurring problem of computer systems’ security with the continual changes and increasing volume of hacking techniques. This paper explores a new countermeasure approach for anomaly-based intrusion detection using a multicriterion fuzzy classification method combined with a greedy attribute selection. The proposed approach has the advantage of dealing with various types of attributes including network traffic basic TCP/IP packet headers, as well as content-based, time-based and host-based attributes. At the same time, to reduce the dimensionality and increase the computational efficiency, the greedy attribute selection algorithm enables it to choose an optimal subset of attributes that is most relevant for detecting intrusive events. The simplicity of the constructed model allows it to be replicated at various network components in emerging open system infrastructures such as sensor networks, wireless ad hoc networks, cloud computing, and smart grids. The proposed approach is evaluated and compared on a commonly-used intrusion detection benchmark dataset. The results show more than 99.9% overall accuracy with high detection rates for various types of intrusions can be achieved with about 26% only of the available attributes.

Udkm1Dsim—A simulation toolkit for 1D ultrafast dynamics in condensed matter

The udkm1Dsim toolbox is a collection of matlab (MathWorks Inc.) classes and routines to simulate the structural dynamics and the according X-ray diffraction response in one-dimensional crystalline sample structures upon an arbitrary time-dependent external stimulus, e.g. an ultrashort laser pulse. The toolbox provides the capabilities to define arbitrary layered structures on the atomic level including a rich database of corresponding element-specific physical properties. The excitation of ultrafast dynamics is represented by an N-temperature model which is commonly applied for ultrafast optical excitations. Structural dynamics due to thermal stress are calculated by a linear-chain model of masses and springs. The resulting X-ray diffraction response is computed by dynamical X-ray theory. The udkm1Dsim toolbox is highly modular and allows for introducing user-defined results at any step in the simulation procedure. Program summaryProgram title: udkm1DsimCatalogue identifier: AERH_v1_0Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AERH_v1_0.htmlProgram obtainable from: CPC Program Library, Queen’s University, Belfast, N. IrelandLicensing provisions: BSDNo. of lines in distributed program, including test data, etc.: 130221No. of bytes in distributed program, including test data, etc.: 2746036Distribution format: tar.gzProgramming language: Matlab (MathWorks Inc.).Computer: PC/Workstation.Operating system: Running Matlab installation required (tested on MS Win XP - 7, Ubuntu Linux 11.04-13.04).Has the code been vectorized or parallelized?: Parallelization for dynamical XRD computations. Number of processors used: 1–12 for Matlab Parallel Computing Toolbox; 1−∞ for Matlab Distributed Computing ToolboxRAM: Matlab’s typical RAM requirement of 196 MB is sufficient for most simulations Classification: 7.8, 7.9, 8.External routines:Optional: Matlab Parallel Computing Toolbox, Matlab Distributed Computing Toolbox Required (included in the package): mtimesx Fast Matrix Multiply for Matlab by James Tursa, xml io tools by Jaroslaw Tuszynski, textprogressbar by Paul ProteusNature of problem:Simulate the lattice dynamics of 1D crystalline sample structures due to an ultrafast excitation including thermal transport and compute the corresponding transient X-ray diffraction pattern.Solution method:The program provides an object-oriented toolbox for building arbitrary layered 1D crystalline sample structures including a rich database of element-specific parameters. The excitation, thermal transport and lattice dynamics are simulated utilizing Matlab’s ODE solver. Alternatively, the lattice dynamics can also be calculated analytically utilizing Matlab eigenproblem solver. The dynamical X-ray diffraction is computed in a parallelized matrix formalism.Restrictions:The program is restricted to 1D sample structures and is further limited to longitudinal acoustic phonon modes and symmetrical X-ray diffraction geometries.Unusual features:The program is highly modular and allows the inclusion of user-defined inputs at any time of the simulation procedure.Running time:The running time is highly dependent on the number of unit cells in the sample structure and other simulation parameters such as time span or angular grid for X-ray diffraction computations. However, the example files are computed in approx. 1–5 min each on a 8 Core Processor with 16 GB RAM available.

CABARET method on unstructured hexahedral grids for jet noise computation

A new scalable high-resolution CABARET MILES method coupled with a porous Ffowcs Williams–Hawking formulation for far-field noise modelling is applied for the computation of flow and noise from a high-speed turbulent jet, which corresponds to a converging axi-symmetric nozzle and static isothermal conditions of the Jet Exhaust Aerodynamics and Noise experiment. Computational simulation results are provided for a range of grid resolutions. The results for simple means, turbulent kinetic energy, and fourth-order velocity correlations are compared with the experimental data and with a reference LES-Smagorinsky calculation from the literature. Far-field-noise predictions are obtained based on several integral control surfaces, which are used for consistency of the model. The far-field noise spectra are also compared with the experiment. The directivity of the far field noise predicted is analysed with the Azimuthal Decomposition Technique and compared with jet noise data from the TsAGI experimental database.