Condor services for the Global Grid:interoperability between Condor and OGSA

In order for existing grid middleware to remain viable it is important to investigate their potentialfor integration with emerging grid standards and architectural schemes. The Open Grid ServicesArchitecture (OGSA), developed by the Globus Alliance and based on standard XML-based webservices technology, was the first attempt to identify the architectural components required tomigrate towards standardized global grid service delivery. This paper presents an investigation intothe integration of Condor, a widely adopted and sophisticated high-throughput computing softwarepackage, and OGSA; with the aim of bringing Condor in line with advances in Grid computing andprovide the Grid community with a mature suite of high-throughput computing job and resourcemanagement services. This report identifies mappings between elements of the OGSA and Condorinfrastructures, potential areas of conflict, and defines a set of complementary architectural optionsby which individual Condor services can be exposed as OGSA Grid services, in order to achieve aseamless integration of Condor resources in a standardized grid environment

Maximum Volume Subset Selection for Anchored Boxes

Let B be a set of n axis-parallel boxes in d-dimensions such that each box has a corner at the origin and the other corner in the positive quadrant, and let k be a positive integer. We study the problem of selecting k boxes in B that maximize the volume of the union of the selected boxes. The research is motivated by applications in skyline queries for databases and in multicriteria optimization, where the problem is known as the hypervolume subset selection problem. It is known that the problem can be solved in polynomial time in the plane, while the best known algorithms in any dimension d>2 enumerate all size-k subsets. We show that: * The problem is NP-hard already in 3 dimensions. * In 3 dimensions, we break the enumeration of all size-k subsets, by providing an n^O(sqrt(k)) algorithm. * For any constant dimension d, we give an efficient polynomial-time approximation scheme

Single- and multi-objective evolutionary design optimization assisted by gaussian random field metamodels

In this thesis numerical optimization methods for single- and multi-objective design optimization with time-consuming computer experiments are studied in theory and practise. We show that the assistance by metamodeling techniques (or: surrogates) can significantly accelerate evolutionary (multi-objective) optimization algorithms (E(M)OA) in the presence of time consuming evaluations. A further increase of robustness can be achieved by taking confidence information for the imprecise evaluations into account. Gaussian random field metamodels, also referred to as Kriging techniques, can provide such confidence information. The confidence information is used to figure out ‘white spots’ in the functional landscape to be explored. The thesis starts with a detailed discussion of computational aspects related to the Kriging algorithm. Then, algorithms for optimization with single objectives, constraints and multiple objectives are introduced. For the latter, with the S-metric selection EMOA (SMS-EMOA) a new powerful algorithm for Pareto optimization is introduced, which outperforms established techniques on standard benchmarks. The concept of a filter is introduced to couple E(M)OA with metamodeling techniques. Various filter concepts are compared, both by means of deducing their properties theoretically and by experiments on artificial landscapes. For the latter studies we propose new analytical indicators, like the inversion metric and the recall/precision measure. Moreover, sufficient conditions for global convergence in probability are established. Finally the practical benefit of the new techniques is demonstrated by solving several industrial optimization problems, including airfoil optimization, solidification process design, metal forming, and electromagnetic compatibility design and comparing the results to those obtained by standard algorithms

On using Markov decision processes to model integration solutions for disparate resources in software ecosystems

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.The software ecosystem of an enterprise is usually composed of an heterogeneous set of applications, databases, documents, spreadsheets, and so on. Such resources are involved in the enterprise’s daily activities by supporting its business processes. As a consequence of market change and the enterprise evolution, new business processes emerge and the current ones have to be evolved to tackle the new requirements. It is not a surprise that different resources may be required to collaborate in a business process. However, most of these resources were devised without taking into account their integration with the others, i.e., they represent isolated islands of data and functionality. Thus, the goal of an integration solution is to enable the collaboration of different resources without changing them or increasing their coupling. The analysis of integration solutions to predict their behaviour and find possible performance bottlenecks is an important activity that contributes to increase the quality of the delivered solutions. Software engineers usually follow an approach that requires the construction of the integration solution, the execution of the actual integration solution, and the collection of data from this execution in order to analyse and predict their behaviour. This is a costly, risky, and time-consuming approach. This paper discusses the usage of Markov models for formal modelling of integration solutions aiming at enabling the simulation of the conceptual models of integration solutions still in the design phase. By using well-established simulation techniques and tools at an early development stage, this new approach contributes to reduce cost, risk, development time and improve software quality attributes such as robustness, scalability, and maintenance

Binding between two-component bosons in one dimension

We investigate the ground state of one-dimensional few-atom Bose-Bose mixtures under harmonic confinement throughout the crossover from weak to strong inter-species attraction. The calculations are based on the numerically exact multi-configurational time-dependent Hartree method. For repulsive components we detail the condition for the formation of a molecular Tonks-Girardeau gas in the regime of intermediate inter-species interactions, and the formation of a molecular condensate for stronger coupling. Beyond a critical inter-species attraction, the system collapses to an overall bound state. Different pathways emerge for unequal particle numbers and intra-species interactions. In particular, for mixtures with one attractive component, this species can be viewed as an effective potential dimple in the trap center for the other, repulsive component.Comment: 10 pages, 10 figure

A Bayesian approach to portfolios selection in multicriteria group decision making

In the a-posteriori approach to multicriteria decision making the idea is to first find a set of interesting (usually non-dominated) decision alternatives and then let the decision maker select among these. Often an additional demand is to limit the size of alternatives to a small number of solutions. In this case, it is important to state preferences on sets. In previous work it has been shown that independent normalization of objective functions (using for instance desirability functions) combined with the hypervolume indicator can be used to formulate such set-preferences. A procedure to compute and to maximize the probability that a set of solutions contains at least one satisfactory solution is established. Moreover, we extend the model to the scenario of multiple decision makers. For this we compute the probability that at least one solution in a given set satisfies all decision makers. First, the information required a-priori from the decision makers is considered. Then, a computational procedure to compute the probability for a single set to contain a solution, which is acceptable to all decision makers, is introduced. Thereafter, we discuss how the computational effort can be reduced and how the measure can be maximized. Practical examples for using this in database queries will be discussed, in order to show how this approach relates to applications

Excitations of attractive 1-D bosons: Binding vs. fermionization

The stationary states of few bosons in a one-dimensional harmonic trap are investigated throughout the crossover from weak to strongly attractive interactions. For sufficient attraction, three different classes of states emerge: (i) N-body bound states, (ii) bound states of smaller fragments, and (iii) gas-like states that fermionize, that is, map to ideal fermions in the limit of infinite attraction. The two-body correlations and momentum spectra characteristic of the three classes are discussed, and the results are illustrated using the soluble two-particle model.Comment: 7 pages, 5 figure

An EAI based integration solution for science and research outcomes information management

Open Access articleIn this paper we present an Enterprise Application Integration (EAI) based proposal for research outcomes information management. The proposal is contextualized in terms of national and international science and research outcomes information management, corresponding supporting information systems and ecosystems. Information systems interoperability problems, approaches, technologies and tools are presented and applied to the research outcomes information management case. A business and technological perspective is provided, including the conceptual analysis and modelling, an integration solution based in a Domain-Specific Language (DSL) and the orchestration engine to execute the proposed solution. For illustrative purposes, the role and information system needs of a research unit is assumed as the representative case