Distributed Computing Infrastructure

Solving workflow management system's Distributed Computing Infrastructure (DCI) incompatibility and their workflow interoperability issues are very challenging and complex tasks. Workflow management systems (and therefore their workflows, workflow developers and also their end-users) are bounded tightly to some limited number of supported DCIs, and efforts required to allow additional DCI support. In this paper we are specifying a concept how to enable generic DCI compatibility for grid workflow management systems (such as ASKALON, MOTEUR, gUSE/WS-PGRADE, etc.) on job and indirectly on workflow level. To enable DCI compatibility among the different workflow management systems we have developed the DCI Bridge software solution. In this paper we will describe its internal architecture, provide usage scenarios to show how the developed service resolve the DCI interoperability issues between various middleware types. The generic DCI Bridge service enables the execution of jobs onto the existing major DCI platforms (such as Service Grids (Globus Toolkit 2 and 4, gLite, ARC, UNICORE), Desktop Grids, Web services, or even cloud based DCIs).

Molecular docking and virtual screening experiments require large computational and data resources and high-level user interfaces in the form of science gateways. While science gateways supporting such experiments are relatively common, there is a clearly identified need to design and implement more complex environments for further analysis of docking results. This paper describes a generic framework and a related methodology that supports the efficient development of such environments. The framework is modular enabling the reuse of already existing components. The methodology, which proposes three techniques that the development team can use, is agile and encourages active participation of end-users. Based on the framework and methodology, two prototype implementations of science-gateway-based docking environments are presented and evaluated. The first system recommends a receptor-ligand pair for the next docking experiment, and the second filters docking results based on ligand pr...

Open-data research is an important factor accelerating the production and analysis of scientific results as well as worldwide collaboration; still, very little data is being shared at scale. The aim of this article is to analyze existing data-access solutions along with their usage limitations. After analyzing the existing solutions and data-access stakeholder needs, the authors propose their own vision of a data-access model.

The paper describes a computer system for simulating metallurgical rolling processes that consist of multiple steps, each of which is performed by a different type of devices. Both devices and processed materials are described with models, which can be dynamically reconfigured between simulation runs to study different device and environment configurations. Such an approach is especially crucial in technology design based on multi-iterative optimization procedures, for which an objective function uses computationally intensive algorithms. Due to the approach proposed in this paper, in the first stage of optimization more general and coarse models can be applied characterized by lower predictive capabilities and higher computational efficiency. Afterwards, when the optimization procedure finds a solution close to the optimal one, very detailed models can be used to obtain high quality solutions in the last few steps of calculations. To achieve such an objective a hybrid computer system able to use High Performance Computing (HPC) infrastructures was designed and implemented. The details of proposed approach are described, which is followed by presentation of a data farming platform responsible for distribution of complex numerical simulations onto various computer clusters. Finally, a concrete use case of a hot rolling mill is presented and analyzed.

Developing and deploying a global and scalable data access service is a challenging task. We assume that the globalization is achieved by creating and maintaining appropriate metadata while the scalability is achieved by limiting the number of entities taking part in keeping the metadata consistency. In this paper, we present different consistency and synchronization models for various metadata types chosen for implementation of global and scalable data access service.

The paper describes a computer system for simulating metallurgical rolling processes that consist of multiple steps, each of which is performed by a different type of devices. Both devices and processed materials are described with models, which can be dynamically reconfigured between simulation runs to study different device and environment configurations. Such an approach is especially crucial in technology design based on multi-iterative optimization procedures, for which an objective function uses computationally intensive algorithms. Due to the approach proposed in this paper, in the first stage of optimization more general and coarse models can be applied characterized by lower predictive capabilities and higher computational efficiency. Afterwards, when the optimization procedure finds a solution close to the optimal one, very detailed models can be used to obtain high quality solutions in the last few steps of calculations. To achieve such an objective a hybrid computer system able to use High Performance Computing (HPC) infrastructures was designed and implemented. The details of proposed approach are described, which is followed by presentation of a data farming platform responsible for distribution of complex numerical simulations onto various computer clusters. Finally, a concrete use case of a hot rolling mill is presented and analyzed.

Основы технической диагностики нефтегазового оборудования Допущено Министерством образования и науки Российской Федерации в качестве учебного пособия для студентов высших учебных заведений, обучающихся по специальностям «Морские нефтегазовые сооружения» и «Машины и оборудование нефтяных и газовых промыслов» направления подготовки «Оборудование и агрегаты нефтегазового производства» Москва 1| «Высшая школа» ifcT# 2006 УДК 621.6 ББ К 30.82 Б73 Рецензенты: кафедра машин и оборудования нефтяных и газовых промыслов Архангельского государственного технического университета (зав. кафедрой А.Э. Абанов); директор ЗАО «Санкт-Петербургская техническая экспертная компания» по на учной и экспертной работе, д-р техн. наук, проф. О.А. Бардышев Богданов, Е. А. Б73 Основы технической диагностики нефтегазового оборудо вания: Учеб. пособие для вузов / Е. А. Богданов.-М.: Высш. шк., 2006.-279 с.: ил. ISBN 5-06-005442-Х В книге изложены основные методы контроля, применяемые при проведе нии технической диагностики газонефтепромыслового оборудования. Рассмот рены методики диагностирования ряда типовых видов оборудования и оценки его остаточного ресурса. Для студентов, обучающихся по специальностям «Морские нефтегазовые со оружения» и «Машины и оборудование нефтяных и газовых промыслов» направле ния подготовки дипломированных специалистов «Оборудование и агрегаты нефте газового производства». Пособие может быть полезно инженерно-техническим работникам производственных предприятий и экспертных организаций, занимаю щихся вопросами диагностики.

Although simulation techniques are widely used to support both mission rehearsal and mission analysis, these two applications tend to be considered as distinctly separate. In this article we argue that integrating them in a unified framework can benefit the end-users of the system (armed forces or police and security forces). We demonstrate this on project EUSAS ("European Urban Simulation of Asymmetric Scenarios") financed by 20 nations under the Joint Investment Program Force Protection of the European Defence Agency, where such a unified infrastructure is being developed. We show how a novel approach to integration through behaviour cloning enables the system to capture the operational knowledge of security experts in a non-verbal way. This capability in fact emerges as essential for the operation of the integrated system, and we illustrate how the interplay between the system components for mission analysis and mission rehearsal is realized.

The paper describes a computer system for simulating metallurgical rolling processes that consist of multiple steps, each of which is performed by a different type of devices. Both devices and processed materials are described with models, which can be dynamically reconfigured between simulation runs to study different device and environment configurations. Such an approach is especially crucial in technology design based on multi-iterative optimization procedures, for which an objective function uses computationally intensive algorithms. Due to the approach proposed in this paper, in the first stage of optimization more general and coarse models can be applied characterized by lower predictive capabilities and higher computational efficiency. Afterwards, when the optimization procedure finds a solution close to the optimal one, very detailed models can be used to obtain high quality solutions in the last few steps of calculations. To achieve such an objective a hybrid computer system able to use High Performance Computing (HPC) infrastructures was designed and implemented. The details of proposed approach are described, which is followed by presentation of a data farming platform responsible for distribution of complex numerical simulations onto various computer clusters. Finally, a concrete use case of a hot rolling mill is presented and analyzed.

The presented state of heating energy equipment in Ukraine are considered. Showed the necessity of creation a modern system of such equipment, which will operate with using retrospective data on the basis of distributed computing infrastructures. Analyzed the modern information and analytical systems of thermal power diagnosis. Suggested the criteria for monitoring the state of the heat power equipment on its actual condition.Институт по механика-БАН, КОНТРОЛ ООД, IQC В.Ничев, МУЛТИТЕСТ, АЕЦ Козлоду

Although simulation techniques are widely used to support both mission rehearsal and mission analysis, these two applications tend to be considered as distinctly separate. In this article we argue that integrating them in a unified framework can benefit the end-users of the system (armed forces or police and security forces). We demonstrate this on project EUSAS ("European Urban Simulation of Asymmetric Scenarios") financed by 20 nations under the Joint Investment Program Force Protection of the European Defence Agency, where such a unified infrastructure is being developed. We show how a novel approach to integration through behaviour cloning enables the system to capture the operational knowledge of security experts in a non-verbal way. This capability in fact emerges as essential for the operation of the integrated system, and we illustrate how the interplay between the system components for mission analysis and mission rehearsal is realized.

In a modern globalised world, military and peace keeping forces often face situations which require very subtle and well planned operations taking into account cultural and social aspects of a given region and its population as well as dynamic psychological awareness related to recent events which can have impact on the attitude of the civilians. The goal of the EUSAS project is to develop a prototype of a system enabling mission planning support and training capabilities for soldiers and police forces dealing with asymmetric threat situations, such as crowd control in urban territory. In this paper, we discuss the data-farming infrastructure developed for this project, allowing generation of large amount of data from agent based simulations for further analysis allowing soldier training and evaluation of possible outcomes of different rules of engagement.

With the increasing availability of high performance computing power, new possibilities with respect to simulation and analysis become available and feasible. One of such methodologies is data farming, where large amounts of data are generated through simulation of several configurations from large parameter space and then analyzed for patterns. Unfortunately, the availability of versatile data farming systems is very limited and none of existing solutions allows integration with novel Cloud solutions. In this paper, we present our system, which is a flexible solution for running very large Data Farming experiments on both intranet clusters as well as on remote computational resources, including public Clouds. Another important aspect of the presented system is the support of interactive Data Farming experiments with online analysis of partial experiment results and experiment extending capability. Sample application of our system is present on military mission planning support scenario.

The paper describes the application of heterogeneous computational infrastructures to study complex metallurgical processes. This goal is achieved by integration of a domain-oriented system (VirtRoll) with a platform for massive parameter studies (Scalarm) on the basis of Service Oriented Architecture (SOA). In particular, technological and security aspects of the integration and permissions delegation are discussed. We describe: a workflow of studying metallurgical processes with the parameter study approach, the domain-oriented system for conducting metallurgical experiments, its integration with the platform for parameter studies along with its enhancements, a sample use of the developed solution and its potential business benefits.

This paper describes a programming toolkit developed in the PL-Grid project, named QStorMan, which supports storage QoS provisioning for data-intensive applications in distributed environments. QStorMan exploits knowledgeoriented methods for matching storage resources to non-functional requirements, which are defined for a data-intensive application. In order to support various usage scenarios, QStorMan provides two interfaces, such as programming libraries or a web portal. The interfaces allow to define the requirements either directly in an application source code or by using an intuitive graphical interface. The first way provides finer granularity, e.g., each portion of data processed by an application can define a different set of requirements. The second method is aimed at legacy applications support, which source code can not be modified. The toolkit has been evaluated using synthetic benchmarks and the production infrastructure of PL-Grid, in particular its storage infrastructure, which utilizes the Lustre file system.

It is now several years since scientists in Poland can use the resources of the distributed computing infrastructure – PLGrid. It is a flexible, large-scale e-infrastructure, which offers a homogeneous, easy to use access to organizationally distributed, heterogeneous hardware and software resources. It is built in accordance with good organizational and engineering practices, taking advantage of international experience in this field. Since the scientists need assistance and close collaboration with service providers, the e-infrastructure is relied on users' requirements and needs coming from different scientific disciplines, being equipped with specific environments, solutions and services, suitable for various disciplines. All these tools help to lowering the barriers that hinder researchers to use the infrastructure.

The paper describes the application of heterogeneous computational infrastructures to study complex metallurgical processes. This goal is achieved by integration of a domain-oriented system (VirtRoll) with a platform for massive parameter studies (Scalarm) on the basis of Service Oriented Architecture (SOA). In particular, technological and security aspects of the integration and permissions delegation are discussed. We describe: a workflow of studying metallurgical processes with the parameter study approach, the domain-oriented system for conducting metallurgical experiments, its integration with the platform for parameter studies along with its enhancements, a sample use of the developed solution and its potential business benefits.

In a modern globalised world, military and peace keeping forces often face situations which require very subtle and well planned operations taking into account cultural and social aspects of a given region and its population as well as dynamic psychological awareness related to recent events which can have impact on the attitude of the civilians. The goal of the EUSAS project is to develop a prototype of a system enabling mission planning support and training capabilities for soldiers and police forces dealing with asymmetric threat situations, such as crowd control in urban territory. In this paper, we discuss the data-farming infrastructure developed for this project, allowing generation of large amount of data from agent based simulations for further analysis allowing soldier training and evaluation of possible outcomes of different rules of engagement.

With the increasing availability of high performance computing power, new possibilities with respect to simulation and analysis become available and feasible. One of such methodologies is data farming, where large amounts of data are generated through simulation of several configurations from large parameter space and then analyzed for patterns. Unfortunately, the availability of versatile data farming systems is very limited and none of existing solutions allows integration with novel Cloud solutions.