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There is a rapid increase in power demand which has led to an increase in the need for smaller grids all over the world. The microgrid is such a small power supply system that consists of loads, distributed energy resources, and energy storage systems. With the development of the internet, the power sector has started the development of the newest type of grids, involving a smart technology called smart grids. Smart grids have got smart integration and advanced storage, along with dynamic optimization of grid operation and resources, digital information and control, distributed resources, demand-side resources, energy efficiency resources, and smart metering systems. The developments in intelligent controllers reflect in power flow management also. This paper gives an idea of how to achieve power flow management through a tariff-based Fuzzy Logic Controller (FLC). The control algorithm used here is the k factor-based Icosϕ algorithm, which decides the amount of power shared by microgrids and the grid based on the load demand. The power forecasting of the microgrid with a solar source is performed using ANN. The regression plot and the error histogram obtained show the accuracy of the ANN controller. The simulation of power flow management of the smart microgrid system is tested and analyzed and the proposed system is compared with other existing research. The hardware implementation of the FLC controller with the Arduino interface which is analogous to real-time measurement is explained in detail to compare the simulation results obtained. [ABSTRACT FROM AUTHOR]

Grid computing is a computational paradigm that emerged to ‎‎handle the increasing demand for ‎computational resources. Several metaheuristics methods ‎‎have been applied ‎to tackle the grid task scheduling problem. ‎‎These metaheuristics generally generate good but not optimal ‎‎task ‎schedules. The aim of this paper is to design and ‎‎implement a grid task scheduling mechanism to map clients' tasks to ‎‎ ‎available resources in order to finish the submitted tasks ‎‎within the optimal execution time. The paper proposes ‎an ‎‎enhanced time shared metaheuristics mechanism based on ‎‎Firefly Algorithm to ‎‎improve the grid job scheduling process. The proposed mechanism utilizes the Smallest Position ‎Value (SPV) technique to handle the scheduling problem as ‎permutations. Experiments using ‎‎simulations and real workload traces were ‎conducted to study ‎‎the performance of the proposed enhanced time shared ‎‎metaheuristic scheduling mechanism. ‎Empirical results revealed ‎‎that the proposed timed shared ‎metaheuristic algorithm can efficiently reduce the makespan time to 1851 compared with 3482, 3185 for Tabu search and genetic algorithm, respectively. [ABSTRACT FROM AUTHOR]

Compressed sensing (CS) algorithms mostly achieve linear array synthetic aperture radar (LASAR) 3D sparse imaging under the On-Grid strategy, they suffer from the mismatch measurement matrix and decreased imaging quality due to the position-errors among targets and preset scattering units. Aimed at this problem, based on the Off-Grid strategy, a sparse recovery algorithm via adaptive grids (SRAG) is proposed in this paper. First, the proposed algorithm extracts the target scattering units in the imaging scene under the On-Grid strategy. Second, the target scattering units with position-errors are extracted under the non-zero adjacent scattering coefficient criterion, and they are defined as the unfocused scattering units. Third, according to whether the unfocused scattering units' distance is smaller than the scattering unit spacing, their search-areas of targets' real positions are set, respectively, and differently. Finally, based on the minimum scattering coefficient residual criterion, we adaptively compensate the unfocused scattering units' position-errors and re-estimate their scattering coefficients. Both simulation and experimental results indicate that the SRAG algorithm decreases the position-errors effectively, it improves the imaging quality, estimation accuracy of targets' positions, and scattering coefficients than traditional On-Grid CS algorithms. [ABSTRACT FROM AUTHOR]

This paper studies the load frequency control (LFC) problem of smart grids under aperiodic denial-of-service (DoS) attacks. First, a switching observer model is constructed to deal with the unavailability of system status. Second, an adaptive event-triggered LFC scheme is proposed, which effectively reduces the update frequency of control signals and reduces the impact of aperiodic DoS attacks simultaneously. Third, by considering the impact of DoS attacks and event-triggered mechanisms at the same time, the system is modelled as a switching model. Then, by constructing a suitable piecewise Lyapunov functional, sufficient conditions are derived to ensure the exponential stability of the system with H ∞ performance. In addition, the joint design method of observer and controller is also developed. Finally, a single-area power system is taken as an example to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Multidimensional torus interconnect finds wide application in modern exascale computing. For models design in high-performance computing, grid and cloud computing, and also systems biology, two basic ways of specifying spatial structures with Petri nets are considered – an infinite Petri net specified by a parametric expression (PE) and a reenterable coloured Petri net (CPN). The paper studies a composition of hypertorus grid models in the form of a PE and a reenterable CPN, their mutual transformations, and unfolding into a place/transition net; the parameters are the number of dimensions and the size of grid. A grid is composed via connection of neighbouring cells by dedicated transitions modelling channels. Reenterable model peculiarities are explained on step-by-step simulation examples. The rules of mutual transformations of Petri net spatial specifications are specified. Comparative investigation of two mentioned forms of spatial specifications is implemented, including analysis techniques and tools. CPNs are convenient for the state space analysis. The main advantage of PEs is the ability to obtain linear invariants and other structural constructs of Petri nets, for instance, siphons and traps, in parametric form that allows us to draw conclusions on Petri net properties for any values of parameters. [ABSTRACT FROM AUTHOR]

In this paper, a methodology is proposed to support water decisions by selecting and evaluating reservoir operating rules (OR) based on hydrological scenarios. The methodology includes optimization and simulation tools within an Implicit Stochastic Optimization (ISO) framework. The paper presents some improvements to the traditional ISO that overcome some limitations affecting previous works. Thanks to the collaboration with Regional Water Authorities in Southern Italy, the proposed methodology, called Modified Implicit Stochastic Optimization (MISO), has been tested in the Agri-Sinni water system. In a participatory and integrated risk management approach to drought events, the reservoir OR defined in the MISO approach based on correlations between releases, storages and inflows performed better than the actual OR in the Agri-Sinni water system and the OR from a simulation-alone procedure. In addition, the user can obtain a significant reduction of computational time by applying the MISO technique in a Grid computing approach. [ABSTRACT FROM AUTHOR]

In this paper, we consider the Fourier spectral method on the sparse grids for computing the ground state of the many-particle fractional Schrödinger equations. The appropriate sparse grids for many-particle fractional Schrödinger equations are given, and the estimation for the number of grid points is obtained. Then, the iterative scheme of the inverse power method is presented to compute the ground state. In the numerical experiments, we consider two kinds of fractional Schrödinger equations, i.e. the fractional Schrödinger equation with harmonic potential and the electronic fractional Schrödinger equation. For the fractional Schrödinger equation with harmonic potential, when the scaled Planck constant ℏ is equal to 1, the sparse grid method has the obvious advantages in the high-resolution approximation both for the integer-order problem and the fractional-order problem, but when ℏ is equal to 1/4, the advantage of sparse grid method exists only for the fractional-order problem. We also show that for the electronic fractional Schrödinger equation, the sparse grid method is much better than the full grid method when the number of electrons is relatively large. [ABSTRACT FROM AUTHOR]

This paper proposes a novel data indexing scheme, the distributed access pattern R-tree (DAPR-tree), for spatial data retrieval in a distributed computing environment. As compared to traditional distributed indexing schemes, the DAPR-tree introduces the data access patterns during the indexing utilization stage so that a more balanced indexing structure can be provided for spatial applications (e.g. Digital Earth data warehouse). In this new indexing scheme, (a) an indexing penalty matrix is proposed by considering the balance of data number, topology and access load between different indexing nodes; (b) an 'access possibility' element is integrated to a classic 'Master-Client' structure for a distributed indexing environment; and (c) indexing algorithm for the DAPR-tree is provided for index implementations. By using a duplication of official GEOSS Clearinghouse system as a case study, the DAPR-tree was evaluated in a number of scenarios. The results show that our indexing schemes generally outperform (around 9%) traditional distributed indices with the utilization of data access patterns. Finally, we discuss the applicability of the DARP-tree and document DARP-tree shortcomings to encourage researchers pursuing related topics in Big Data indexing for Digital Earth and other geospatial initiatives. [ABSTRACT FROM AUTHOR]

This paper presents a grid-based scan-to-map matching technique for accurate 2D map building. At every acquisition of a new scan, the proposed technique matches the new scan to the previous scan similarly to the conventional techniques, but further corrects the error by matching the new scan to the globally defined map. In order to achieve best scan-to-map matching at each acquisition, the map is represented as a grid map with multiple normal distributions (NDs) in each cell, which is one contribution of this paper. Additionally, the new scan is also represented by NDs, developing a novel ND-to-ND matching technique. This ND-to-ND matching technique has significant potential in the enhancement of the global matching as well as the computational efficiency. Experimental results first show that the proposed technique accumulates very small errors after consecutive matchings and identifies that the scans are matched better to the map with the multi-ND representation than one ND representation. The proposed technique is then tested in a number of large indoor environments, including public domain datasets and the applicability to real world problems is demonstrated. [ABSTRACT FROM PUBLISHER]

Abstract—Harmonic levels in distribution power systems are expected to rise in the future. Understanding the consumer load composition of each individual circuit and maximum harmonic limits can help utilities plan and mitigate power quality issues. This paper presents a sensitivity analysis approach of IEEE Standard 519 total harmonic distortion (THD) current limits on an actual utility model. A new dynamic harmonic index method for characterizing distribution power circuits is proposed, which utilizes the short circuit ratio (SCR) as the main parameter. OpenDSS is used as a modeling platform to study the THD of the voltage (THDv), while increasing the harmonic spectrum of random consumer load points. The main goals are to: (1) identify voltage behavior in power utility networks when an increasing number of consumers operate at the Std. 519 limits, and (2) categorize utility circuits using available information. The short circuit at the point of common coupling, maximum demand load currents, and the circuit configuration are considered. Three actual utility circuits are analyzed and compared in terms of their SCR and one of these circuits is analyzed in depth as part of the sensitivity study. The proposed method is also compared with the conventional pure capacitor and tuned filter bank methods. Finally, a recommendation for new harmonic distortion current limits is provided. [ABSTRACT FROM PUBLISHER]

This paper focuses on two common problems encountered when using Light Detection And Ranging (LiDAR) data to derive digital elevation models (DEMs). Firstly, LiDAR measurements are obtained in an irregular configuration and on a point, rather than a pixel, basis. There is usually a need to interpolate from these point data to a regular grid so it is necessary to identify the approaches that make best use of the sample data to derive the most accurate DEM possible. Secondly, raw LiDAR data contain information on above‐surface features such as vegetation and buildings. It is often the desire to (digitally) remove these features and predict the surface elevations beneath them, thereby obtaining a DEM that does not contain any above‐surface features. This paper explores the use of geostatistical approaches for prediction in this situation. The approaches used are inverse distance weighting (IDW), ordinary kriging (OK) and kriging with a trend model (KT). It is concluded that, for the case studies presented, OK offers greater accuracy of prediction than IDW while KT demonstrates benefits over OK. The absolute differences are not large, but to make the most of the high quality LiDAR data KT seems the most appropriate technique in this case. [ABSTRACT FROM AUTHOR]

This paper studies task allocation in computational grids operating in a dynamic and uncertain environment. Computational grids consist of loosely coupled heterogeneous resources or agents with finite buffer capacities. These grids are primarily used to process large-scale applications consisting of several interdependent tasks. The task allocation problem is modeled as an infinite horizon Markov decision process, with the resource service times and the task arrivals following general probability distributions. We explicitly consider the communication cost between agents incurred in coordinating the execution of interdependent tasks. We show that a stationary optimal policy exists for this task allocation problem. Furthermore, we develop an action elimination procedure for reducing the complexity of computational methods in finding the optimal policy. We also present a real-time heuristic policy based on certain structural properties of the problem. Finally, computational results are presented that compare the performance of the heuristic policy with respect to other approaches for allocating tasks in the grid. Results from this paper are also applicable to the task allocation problem in manufacturing and service areas such as distributed design, project management and supplier coalitions. [ABSTRACT FROM AUTHOR]

A grid is a distributed network of computers that can be used as a single information and computational resource for multiple datasets and sources. One of the key challenges of grid computing is that information in different datasets and different types of data such as text and image are likely to have different levels of sensitivity, disclosure risk and terms of use. This paper provides an overview of the development of grid computing and an examination of the disclosure risks poses. The research, funded under the Economic and Social Research Council's (ESRC) e-Social Science Programme, specifically examined the development of grid computing in social science and the work of the e-Social Science grid pilot projects. The paper concludes that while a number of innovative grid enablement projects are in development there has only been a limited assessment of the additional disclosure risks grid computing may pose. The incorporation of effective confidentiality and disclosure control systems will be central in encouraging data holders to share their data and computational resources. [ABSTRACT FROM AUTHOR]

Public service systems, such as emergency health care, police or fire brigades, are critical for day-to-day functioning of the society. To design and operate these systems efficiently much data needs to be collected and properly utilised. Here, we use the OpenStreetMap (OSM) data to model the demand points (DPs), which approximate the geographical location of customers, and the road network, which is used to access or distribute services. We consider all inhabitants as customers, and therefore to estimate the demand, we use the available population grids. People are changing their location in the course of the day and thus the demand for services is changing accordingly. In this paper, we investigate how the used demand estimate affects the optimal design of a public service system. We calculate and compare efficient designs corresponding to two demand models, a night-time demand model when the majority of inhabitants rest at home and the demand model derived from the 24-hour average of the population density. We propose a simple measure to quantify the differences between population grids and we estimate how the size of differences affects the optimal structure of a public service system. Our analyses reveal that the efficiency of the service system is not only dependent on the placement strategy, but an inappropriate demand model has significant effects when designing a system as well as when evaluating its efficiency. [ABSTRACT FROM PUBLISHER]

In this paper, a recently developed regression-based option pricing method, the Stochastic Grid Bundling Method (SGBM), is considered for pricing multidimensional Bermudan options. We compare SGBM with a traditional regression-based pricing approach and present detailed insight in the application of SGBM, including how to configure it and how to reduce the uncertainty of its estimates by control variates. We consider the Merton jump-diffusion model, which performs better than the geometric Brownian motion in modelling the heavy-tailed features of asset price distributions. Our numerical tests show that SGBM with appropriate set-up works highly satisfactorily for pricing multidimensional options under jump-diffusion asset dynamics. [ABSTRACT FROM PUBLISHER]

With increasing use of geosynthetic applications in earth structures the need to develop more efficient reinforcement elements becomes evident. In this paper a conventional and an innovative geogrid system (named Grid-Anchor) are tested. The Grid-Anchor system consists of a conventional geogrid with anchors attached to it. The pull-out test has been used to highlight the capabilities of the product. Experimental investigation along with numerical studies using a finite element computer code was carried out. It was found that the ultimate pull-out resistance of a Grid-Anchor is more than that for an ordinary geogrid. Analytical study has been performed and the effect of an anchor group on the ultimate resistance of a geogrid was investigated. [ABSTRACT FROM PUBLISHER]

With the wide adoption of big spatial data and the emergence of CyberGIS, the nontrivial computational intensity introduced by massive amount of data poses great challenges to the performance of vector map visualization. The parallel computing technologies provide promising solutions to such problems. Evenly decomposing the visualization task into multiple subtasks is one of the key issues in parallel visualization of vector data. This study focuses on the decomposition of polyline and polygon data for parallel visualization. Two key factors impacting the computational intensity were identified: the number of features and the number of vertices of each feature. The computational intensity transform functions (CITFs) were constructed based on the linear relationships between the factors and the computing time. The computational intensity grid (CIG) can then be constructed using the CITFs to represent the spatial distribution of computational intensity. A noninterlaced continuous space-filling curve is used to group the lattices of CIG into multiple sub-domains such that each sub-domain entails the same amount of computational intensity as others. The experiments demonstrated that the approach proposed in this paper was able to effectively estimate and spatially represent the computational intensity of visualizing polylines and polygons. Compared with the regular domain decomposition methods, the new approach generated much more balanced decomposition of computational intensity for parallel visualization and achieved near-linear speedups, especially when the data is greatly heterogeneously distributed in space. [ABSTRACT FROM PUBLISHER]

We focus in this paper on the effect of the resolution of direct numerical simulations (DNS) on the spatio-temporal development of the turbulence downstream of a single square grid. The aims of this study are to validate our numerical approach by comparing experimental and numerical one-point statistics downstream of a single square grid and then investigate how the resolution is impacting the dynamics of the flow. In particular, using theQ–Rdiagram, we focus on the interaction between the strain-rate and rotation tensors, the symmetric and skew-symmetric parts of the velocity gradient tensor, respectively. We first show good agreement between our simulations and hot-wire experiment for one-point statistics on the centreline of the single square grid. Then, by analysing the shape of theQ–Rdiagram for various streamwise locations, we evaluate the ability of under-resolved DNS to capture the main features of the turbulence downstream of the single square grid. [ABSTRACT FROM AUTHOR]

Metadata are the information about and description of data. In Digital Earth, metadata become variant and heterogeneous with many uncertainties. This paper studies uncertain features in the generation and application of metadata, and two types of uncertainties (incomplete and imprecise) are described based on semantic quantitative measurement method semantic relationship quantitative measurement based on possibilistic logic and probability statistic (SRQ-PP). Moreover, in the case study, we apply two types of quantitative measurements based on SRQ-PP to describe incomplete (uncertain) knowledge and imprecise (vague) information separately in spatial data service retrieval, which in turn is helpful to identify additional potential data resources and provide a quantitative analysis of the results. [ABSTRACT FROM PUBLISHER]

Autonomous navigation of legged robots in complex environments poses a great deal of challenges compared with ground vehicles because of their different terrain traverse capabilities. An obstacle for vehicles may be traversable for legged robots. This paper proposes a real-time obstacle detection algorithm for legged robots using the Microsoft Kinect sensor. First, the elevation map of a reference grid is calculated. Then an obstacle definition for legged robots is proposed, which makes it possible for a legged robot to discriminate traversable areas from non-traversable areas. To reduce computational cost, sometimes, efficient judging rules are developed to identify obstacles. A spiral search strategy is proposed to find the most ground-like point as the starting point for graph-based traversal. Breadth-First-Traversal of the graph is used to label all traversable areas connecting to the starting point. Experimental results demonstrate that our algorithm is reliable and efficient. The proposed algorithm can be employed in real-time obstacle detection for legged robots in complex environments. [ABSTRACT FROM AUTHOR]

In this paper, we present a deblocking algorithm that removes grid and staircase artefacts, which result from the compression of images. The KORea Multi-Purpose SATellite-2 images are efficiently compressed using an algorithm that assigns quantisation coefficients, by rows, according to local region complexity, in such a way that more complicated regions are more compressed. However, this approach is problematic in that it produces blocking artefacts in those relatively less complicated areas within the same row as the complicated regions. Removing these artefacts with a general deblocking algorithm can, undesirably, blur the complicated regions as well as the artefacts; additionally, the general filter is poor at preserving curved edges. Therefore, the algorithm proposed herein presents an adaptive filtering scheme for removing blocking artefacts while preserving image details, including curved edges, using a content analysis. Particularly, edge adaptive filtering has been introduced to remove staircase artefacts around the big image edges. Experimental analyses revealed that the proposed method outperforms several state-of-the-art approaches in terms of subjective or objective image quality, and computational speed. [ABSTRACT FROM AUTHOR]

The digital spaces have some properties that are not present in the Euclidean space. A digitized circle do not necessarily have the smallest (digital arc length) perimeter of all objects having a given area. In digital geometry various measures of perimeter and area lead to various definitions of digital circles using the digital version(s) of the isoperimetric inequality. Usually the square grid is used as digital plane with either the cityblock or the chessboard neighbourhood relation. In this paper the triangular grid is also used with two types of neighbourhood relation that play importance in Jordan curves. We search for those (digital) objects that have optimal measures and therefore they can be considered as digital circles by our definition. We show that special, (almost) regular hexagons are Pareto optimal, i.e. they fulfil both versions of the isoperimetric inequality: they have maximal area among objects having perimeter at most a given length; and they have minimal perimeter among objects enclosing at most a certain area. The optimal objects can be build in a similar way as the Wang-spiral for the square grid. [ABSTRACT FROM AUTHOR]

This paper studies a numerical scheme for approximating solutions of incompressible magnetohydrodynamic (MHD) equations that uses eddy viscosity stabilization only on the small scales of the fluid flow. This stabilization scheme for MHD equations uses a Galerkin finite element spatial discretization with Scott-Vogelius mixed finite elements and semi-implicit backward Euler temporal discretization. We prove its unconditional stability and prove how the coarse mesh can be chosen so that optimal convergence can be achieved. We also provide numerical experiments to confirm the theory and demonstrate the effectiveness of the scheme on a test problem for MHD channel flow. [ABSTRACT FROM PUBLISHER]

Distributed information retrieval methods are growing rapidly because of the rising need to access and search distributed digital documents. However, the content-based information retrieval (CBIR) is concentrated to extract and retrieve the information from massive digital libraries, which require a huge amount of computing and storage resources. The grid computing provides the reliable infrastructure for effective and efficient retrieval on these large collections. In order to build an effective and efficient CBIR technique, varieties of architectures were developed based on grid technologies. The goal of such architecture is to solve interoperability and heterogeneous resource issues, and increase the efficiency and effectiveness of information retrieval (IR) techniques by harnessing the grid computing capabilities. This paper reviews and analyzes latest research carried out in the domain of large-scale dataset IR based on a grid. The evaluation is based on scalability, response time, scope, data type, search technique, middleware, and query type. The contribution is to illustrate the features, capabilities, and shortages of current solutions that can guide the researchers in this evolving area. [ABSTRACT FROM AUTHOR]

Previous urban structure classification methods are intractable for practical robots in two viewpoints: storing point clouds and complex computation for using conditional random fields. This paper presents a classification method based on normal distribution transform (NDT) grids for practical robots. NDT grids store point clouds in the form of the mean and covariance, instead of directly dealing with huge point clouds. By taking the advantage of NDT grids, we design geometric-featured voxel (GFV) based on NDT grids to represent urban structures as a voxel model. The proposed method consists of three steps: GFV generation, segmentation, and classification. In the segmentation, GFVs are clustered according to units of urban structures by spectral clustering. For the classification, the clustered GFVs are classified as one kind of urban structures by supervised learning. Geometric characteristics of urban structures are expressed by a histogram of geometric words. Experimental results prove that the proposed method based on NDT grids is suitable for practical robots in terms of memory requirement, computation time, and even classification accuracy. [ABSTRACT FROM PUBLISHER]

As grid technique is growing rapidly in recent years, large-scale grid systems usually have the capability of providing flexible, secure, coordinated resource-sharing and problem solving among dynamic virtual organizations. Grid resource discovery (GRD) is one of the crucial issues in the whole system and the discovery models and strategies have a vital influence on the performance of grid systems. In this paper, the definition and principles of GRD are explained first. Then, several existing GRD models, namely centralized, hierarchical, Peer-to-Peer (P2P)-based, group-clustering, and semantic-based models, are introduced in detail from both system architecture and resource matching pattern aspects. In particular, the strategies to increase the searching performance in these models are presented. Moreover, trust mechanism to ensure secure communications in P2P-based GRD models is emphasized. Besides, the comparative analysis among the GRD models is made. Finally, several promising research directions of GRD are provided. [ABSTRACT FROM AUTHOR]

Security is a principal concern in offering an infrastructure for the formation of general-purpose computational grids. A number of grid implementations have been devised to deal with the security concerns by authenticating the users, hosts and their interactions in an appropriate fashion. Resource management systems that are sophisticated and secured are inevitable for the efficient and beneficial deployment of grid computing services. The chief factors that can be problematic in the secured selection of grid resources are the wide range of selection and the high degree of strangeness. Moreover, the lack of a higher degree of confidence relationship is likely to prevent efficient resource allocation and utilisation. In this paper, we present an efficient approach for the secured selection of grid resources, so as to achieve secure execution of the jobs. This approach utilises trust and reputation for securely selecting the grid resources. To start with, the self-protection capability and reputation weightage of all the entities are computed, and based on those values, the trust factor (TF) of all the entities are determined. The reputation weightage of an entity is the measure of both the user's feedback and other entities' feedback. Those entities with higher TF values are selected for the secured execution of jobs. To make the proposed approach more comprehensive, a novel method is employed for evaluating the user's feedback on the basis of the existing feedbacks available regarding the entities. This approach is proved to be scalable for an increased number of user jobs and grid entities. The experimentation portrays that this approach offers desirable efficiency in the secured selection of grid resources. [ABSTRACT FROM PUBLISHER]

In Grids, single user-based brokers focus on meeting individual user job's quality of service requirements such as minimising the cost and time without considering demands from other users. This results in contention for resources and suboptimal schedules. Meta-scheduling in Grids aims to address this scheduling problem, which is NP-hard due to its combinatorial nature. Thus, many heuristic-based solutions using genetic algorithm (GA) have been proposed, apart from traditional algorithms such as greedy and first come first serve. In this paper, we propose the need for a ‘meta-brokering system’ and present a Meta-Broker which schedules multiple jobs on utility Grids. First, we present the architecture of our Meta-Broker and discuss the requirements and functionalities of the Meta-Broker. We, then, propose a linear programming (LP)/integer programming model for scheduling user jobs to multiple resources. We also propose a novel algorithm LP-driven GA which combines the capabilities of LP and GA. The aim of this algorithm is to obtain the best meta-schedule that minimises the combined cost of all users in a coordinated manner. Simulation results show that in comparison to single user-based brokers such as Gridbus and GRUBER, our proposed integrated algorithm offers the best schedule having the minimum processing cost with negligible time overhead. [ABSTRACT FROM PUBLISHER]

The aim of this review paper is to encourage the drying community to discover and use modern tools derived from the fields of applied mathematics and physics. Among the computational methods, multiscale and multiphysics approaches are proposed as operational tools to address the practical problems of optimization, design, and innovation encountered by the industry. On a more fundamental level, some methods from the large family of meshless techniques offer vast potential for investigations at the pore level. On the experimental side, many amazing tools have emerged recently from the field of physics. Several time-resolved imaging possibilities are presented here. Among such techniques, X-ray micro-tomography is perfect for an accurate 3-D description of pore morphology, whereas NMR imaging is well adapted to obtaining time-resolved 3-D images of the moisture content field or other variable fields. [ABSTRACT FROM AUTHOR]

In this paper, we propose a characteristic centred finite difference method on non-uniform grids to solve the problem of the air pollution model. Numerical solutions and error estimates of the air pollution concentration and its first-order derivatives for space variables are obtained. The computational cost of the method is the same as that of the characteristic difference method based on a linear interpolation. The error order of the numerical solutions is the same as that of the characteristic difference method based on a quadratic interpolation. At last, we give numerical examples to illustrate feasibility and efficiency of this method. [ABSTRACT FROM AUTHOR]

The study of global climate change seeks to understand: (1) the components of the Earth's varying environmental system, with a particular focus on climate; (2) how these components interact to determine present conditions; (3) the factors driving these components; (4) the history of global change and the projection of future change; and (5) how knowledge about global environmental variability and change can be applied to present-day and future decision-making. This paper addresses the use of high-performance computing and high-throughput computing for a global change study on the Digital Earth (DE) platform. Two aspects of the use of high-performance computing (HPC)/high-throughput computing (HTC) on the DE platform are the processing of data from all sources, especially Earth observation data, and the simulation of global change models. The HPC/HTC is an essential and efficient tool for the processing of vast amounts of global data, especially Earth observation data. The current trend involves running complex global climate models using potentially millions of personal computers to achieve better climate change predictions than would ever be possible using the supercomputers currently available to scientists. [ABSTRACT FROM AUTHOR]

As a new distributed heterogeneous computing platform, grid aims at achieving Internet-wide resource sharing and collaborative computing. Grid task scheduling (GTS) is the key issue of grid computing, and its algorithm has a direct effect on the performance of the whole system. In this paper, two key entities in GTS, applications and target systems, are defined first. And then two types of the most popular GTS algorithms, namely, meta-task GTS algorithm and directed acyclic graph GTS algorithm, are discussed in details in accordance with the classification of the traditional deterministic algorithm and heuristic intelligent algorithm. In addition, the comparative analysis is made among them. Finally, some main research directions of GTS are pointed out. [ABSTRACT FROM AUTHOR]

In this paper, we report a robust and low-cost navigation algorithm for an unknown environment based on integration of a grid-based map building algorithm with behavior learning. The study focuses on mobile robots that utilize ultrasonic sensors as their prime interface with the outside world. The proposed algorithm takes into account environmental information to augment the readings from the low angular accuracy sonar measurements for behavior learning. The environmental information is obtained by an online grid-based map learning design that is concurrently operating with the behavior learning algorithm. The proposed algorithm is implemented and tested on an in-house-built mobile robot, and its performance is verified through online navigation in an indoor environment. [ABSTRACT FROM AUTHOR]

Grid computing is deemed as a good solution to the digital earth infrastructure. Various geographically dispersed geospatial resources can be connected and merged into a 'supercomputer' by using the grid-computing technology. On the other side, geosensor networks offer a new perspective for collecting physical data dynamically and modeling a real-time virtual world. Integrating geosensor networks and grid computing in geosensor grid can be compared to equipping the geospatial information grid with 'eyes' and 'ears.' Thus, real-time information in the physical world can be processed, correlated, and modeled to enable complex and advanced geospatial analyses on geosensor grid with capability of high-performance computation. There are several issues and challenges that need to be overcome before geosensor grid comes true. In this paper, we propose an integrated framework, comprising the geosensor network layer, the grid layer and the application layer, to address these design issues. Key technologies of the geosensor grid framework are discussed. And, a geosensor grid testbed is set up to illustrate the proposed framework and improve our geosensor grid design. [ABSTRACT FROM AUTHOR]

This paper describes a novel map representation called Inclined Surface Grid (ISG) maps that provides the ability of modeling traversability of an environment. In order to accomplish safer navigation autonomously with a planetary rover by avoiding hazardous areas, appropriate traversability of the terrain has to be evaluated. In the ISG maps, the patch on each grid stores not only traversability but more detailed information — height, slopes (roll and pitch angle) and roughness. The ISG maps have higher degree-of-freedom knowledge about the surface compared with two-dimensional grid or elevation maps and have very good performance to analyze traversability. In order to assure the reliability of surface estimation in far range areas where the point cloud density decreases, an expanding sampling area approach is introduced to homogenize the point density in a grid by increasing the sampling points. Moreover, probabilistically updating using a stereo vision-specified error model is introduced to estimate the information of each patch. Thus, probabilistic updating allows the ISG maps to yield few failed patches. In the experiment, the resulting geometrical information of our ISG maps converged into an accurate geometry of terrain more than the simple overwriting approach. It is shown that our approach can also create the appropriate traversability map in rough terrain. [ABSTRACT FROM AUTHOR]

Distributed geographic information processing (DGIP) refers to the processing of geographic information across dispersed processing units through computer networks and other communication channels. DGIP has become increasingly important in the past decade with the popularization of computer networks, the growth of distributed data repositories, and the collaboration of researchers, developers, and users among multiple disciplines using geographic information. DGIP focuses on the technical research on how to allocate and process geographic information resources in a distributed environment to achieve a specific application objective (such as the implementation of virtual globes). The geographic information resources may include sensors, geographic data, models, information, knowledge, visualization tools, computers, computer networks, software components, architecture, security strategies, applications, and human resources. This introduction to DGIP research defines six research areas: (a) DGIP architecture, including service-oriented architecture (SOA) and Federal Enterprise Architecture (FEA), (b) spatial computing issues for leveraging and allocating computing power to process geographic information, (c) geographic information-processing models for decoupling and integrating models for specific or cross application domains, (d) interoperability, defining the standards and interfaces for sharing processing units, (e) intelligence in DGIP for leveraging knowledge, and (f) applied sciences. The papers selected for this special issue cover all six areas. DGIP will become increasingly important with the globalization of our daily lives across planet Earth and the need to leverage distributed geographic information resources for problem solving and decision making in the global environment. [ABSTRACT FROM AUTHOR]

In Grids, users may require assurance for completing their jobs on shared resources. Such guarantees can only be provided by reserving resources in advance. However, if many reservation requests arrive at a resource simultaneously, the overhead of providing such service due to adding, deleting and searching, will be significant. An efficient data structure for managing these reservations plays an important role in order to minimise the time required for searching available resources, adding and deleting reservations. In this paper, we present new approaches to advance reservation in order to deal with the limitations of the existing data structures, such as Segment Tree and Calendar Queue in similar problems. We propose a Grid advanced reservation Queue (GarQ), which is a new data structure that improves some weaknesses of the aforementioned data structures. We demonstrate the superiority of the proposed structure by conducting a detailed performance evaluation on real workload traces. [ABSTRACT FROM AUTHOR]

Service level agreements (SLAs) are currently one of the major research topics in grid computing. Among many system components for supporting SLA-aware grid-based workflow, the SLA mapping mechanism receives a prominent position. It is responsible for assigning sub-jobs of the workflow to grid resources in a way that meets the user's deadline and minimises costs. Assuming many different kinds of sub-jobs and resources, the process of mapping a light communication workflow within an SLA context defines an unfamiliar and difficult problem. This paper presents a mapping algorithm, which can cope with this problem. The quality and efficiency of the algorithm is demonstrated through performance measurements. [ABSTRACT FROM AUTHOR]

After the set-up of a spatial data infrastructure (SDI) and a national information infrastructure (NII) in many countries, the provision of geo-services became one of the most important and attractive tasks. With the integration of global positioning system (GPS), geographic information system (GIS) and remote sensing (RS), we can, in principle, answer any geo-spatial related question: when and where what object has which changes? An intelligent geo-service agent could provide end-users with the most necessary information in the shortest time and at the lowest cost. Unfortunately there is still a long way to go to achieve such goals. The central component in such geo-services is the integration of the spatial information system with a computing grid via wire- and wireless communication networks. This paper will mainly discuss the grid technology and its integration with spatial information technology, expounding potential problems and possible resolutions. A novel categorising of information grids in the context of geo-spatial information is proposed: generalised and specialised spatial information grids. [ABSTRACT FROM AUTHOR]

Workflow management enables grid service composition and user collaboration in grid middleware, it includes the workflow of grid service invoking and data exchanging among services. Because of the large requirements of grid service composition, it's necessary for the workflow management to be scalable in many grid application systems. This paper proposes a distributed workflow management model for grid middleware, it is comprised of three modules including workflow definition tool (WDT), workflow balancer (WB), workflow execution engine (WEE) and defines the interfaces between workflow management and other grid components. Firstly, WDT composites grid services into workflow service, secondly WB dispatches workflow services and workflow job requests, thirdly WEE is up to the execution of workflow jobs. Furthermore, it describes the interfaces between workflow management and related grid components, such as job management, information service and grid portal. Also it divides the interfaces into WEE management, workflow service management and workflow job management. With the implementation and experiments of this model for Chinese education and research, its support platform middleware, its usability is verified through the usage of workflow tools in image processing scenario, its scalability and load balancing ability are proved in workflow scheduling test. [ABSTRACT FROM AUTHOR]

A pervasive problem seen in information management is information fragmentation, where information may be fragmented by physical location, device or even by the various tools designed to help manage it. Coupled with the explosion of information we see today, identifying information that is really important to us becomes difficult. In areas such as Network and Grid system management, this problem hinders our ability to plan and make intelligent decisions. Grid computing is particularly affected due to the inherent difficulty in dealing with distributed heterogeneous resources over various administrative domains. In this paper we present Gridscape II, a customisable portal component that can be used on its own or plugged-in to compliment existing Grid portals. Gridscape II manages the gathering of information from arbitrary, heterogeneous and distributed sources and presents them together seamlessly within a single interface. To provide an interactive user interface we leverage the Google Maps API, which has recently been adopted extensively as a highly-effective and innovative means of presenting information with geographic location. Gridscape II is simple and easy to use, providing a solution to those users who do not wish to invest heavily in developing their own monitoring portal from scratch, and also for those users who want something easy to customise and extend for their specific needs. Its simple and generic design means it can also directly be used in other, non-Grid related applications. [ABSTRACT FROM AUTHOR]

This paper presents the current state of a computational steering system for interactive computational fluid dynamics (CFD) simulations, allowing engineers to simulate interactively indoor climate and to evaluate human comfort. The tools presented support cooperative planning and design by providing means for interactively adding, removing and modifying geometry and boundary conditions online during a CFD simulation. To ensure interactivity and short-latency updates even for high-resolution runs the parallel Lattice-Boltzmann-based simulation kernel is optimized for high-performance computing systems. Emphasis is placed on the computational steering architecture, connecting a supercomputer with one or more visualization workstations. In particular, we show how a high-performance communication between simulation and visualization or steering front-end can be achieved together with preserving a flexible mechanism of on-the-fly attachment of multiple cooperation clients. [ABSTRACT FROM AUTHOR]

In recent years, Grid computing evolved from first implementations as prototype Grid environments to large-scale production Grid infrastructures utilised during everyday work by scientists around the world. This demonstrates that the concept of the Grid is more than merely a marketing phrase, but instead an enabler for new application domains in parallel and distributed computing. Among others, the EU project EGEE ("Enabling Grids for E-Science") is probably the worlds largest initiative for establishing a permanent Grid infrastructure on a 24 × 7 basis. Such a Grid that is always on and there to serve the community just like the ubiquitous networking infrastructures today is on the horizon, waiting for users to utilise it in their applications. This paper explores the different possibilities for utilising Grids in parallel applications, focusing on different parallel computing aspects as provided by Grid environments. Basic concepts such as using the Grid as a large data storage and management basis or as a collection of distributed supercomputers represent a new approach to computational science, where users are expecting to utilise unprecedented amounts of performance for tackling their scientific problems. [ABSTRACT FROM AUTHOR]

The design of the hierarchical hybrid grids (HHG) framework is motivated by the desire to achieve high performance on large-scale, parallel, finite element simulations on super computers. In order to realize this goal, careful analysis of the low-level, computationally intensive algorithms used in implementing the library is necessary. This analysis is primarily concerned with identifying and removing bottlenecks that limit the serial performance of multigrid component algorithms such as smoothing and residual error calculation. To aid in this investigation, two metrics have been developed: the balance metric (BM), and the loads per miss metric (LPMM). Each of these metrics makes assumptions about the interaction of various data structures and algorithms with the underlying memory subsystems and processors of the architectures on which they are implemented. Applying these metrics generates performance predictions that can then be compared to measured results to determine the actual characteristics of an algorithm/data structure on a given platform. This information can then be used to increase performance. In this paper, we first present an overview of the HHG framework. Next, we introduce the details of the two performance metrics. These metrics are then applied to three different data structures used to implement a Gauß-Seidel smoothing algorithm. Performance results and an interpretation of the underlying interactions of the data structures with several relevant supercomputing architectures are given. Finally, we present a brief discussion of some performance results of the HHG framework, followed by some concluding remarks. [ABSTRACT FROM AUTHOR]

Model selection is concerned with the choice of a mathematical model from a set of candidates that best describes a given set of experimental data. Large families of models arise in the context of structured mechanistic modelling in several application fields. In this situation the model selection problem cannot be solved by brute force testing of all possible models because of the high computational costs. However, more information on the different models of a family is available by their interdependencies, given by generalization or simplification relations. Large-scale model selection algorithms should exploit these relations for navigation in the discrete space of all model candidates. This paper presents a general approach for large-scale model selection by specifying the necessary computational primitives for navigating in large model families. As a non-trivial example it is shown how families of biochemical network models arising from the evaluation of stimulus response experiments are mapped to the general formalism. Finally, a first model selection algorithm based on the mentioned computational primitives is introduced and applied to complex biochemical network experiments. It is based on a load-balancing algorithm by making use of grid computing facilities. [ABSTRACT FROM AUTHOR]

We use the example of a study of the compressibility anomaly in amorphous silica to illustrate how molecular-scale simulations can be performed using grid computing. The potential for running many simulations within a single study requires the use of new data management methods, which are discussed in this paper. The example of silica highlights the advantages of the use of grid computing for studying subtle effects. [ABSTRACT FROM AUTHOR]

We use the example of a study of the compressibility anomaly in amorphous silica to illustrate how molecular-scale simulations can be performed using grid computing. The potential for running many simulations within a single study requires the use of new data management methods, which are discussed in this paper. The example of silica highlights the advantages of the use of grid computing for studying subtle effects. [ABSTRACT FROM AUTHOR]

This paper discusses a number of aspects of using grid computing methods in support of molecular simulations, with examples drawn from the e Minerals project. A number of components for a useful grid infrastructure are discussed, including the integration of compute and data grids, automatic metadata capture from simulation studies, interoperability of data between simulation codes, management of data and data accessibility, management of jobs and workflow, and tools to support collaboration. Use of a grid infrastructure also brings certain challenges, which are discussed. These include making use of boundless computing resources, the necessary changes, and the need to be able to manage experimentation. [ABSTRACT FROM AUTHOR]

This paper describes a prototype grid infrastructure, called the “ e Minerals minigrid”, for molecular simulation scientists. which is based on an integration of shared compute and data resources. We describe the key components, namely the use of Condor pools, Linux/Unix clusters with PBS and IBM's LoadLeveller job handling tools, the use of Globus for security handling, the use of Condor-G tools for wrapping globus job submit commands, Condor's DAGman tool for handling workflow, the Storage Resource Broker for handling data, and the CCLRC dataportal and associated tools for both archiving data with metadata and making data available to other workers. [ABSTRACT FROM AUTHOR]