Web-based simulating system for modeling earthquake seismic wavefields on the grid

We have developed the SYNSEIS (SYNthetic SEISmogram) tool within the GEON (GEOscience Network) project to enable efficient computations of synthetic seismic waveforms for research and education. SYNSEIS is built as a distributed system to support the calculation of synthetic seismograms in 2D/3D media. The underlying simulation software is a finite difference code, E3D, developed by LLNL (S. Larsen). This code is embedded within the SYNSEIS environment and used by our SYNSEIS tool to simulate seismic waveforms of either earthquakes or explosions at regional distances (<1000 km). The SYNSEIS architecture is based around a Web service model. Especially, the computing Web services seamlessly access Grid computing resources by hiding the complexity of grid technologies. Even though the Grid computing is well-established in many computing communities, its use among domain scientists still is not trivial because of multiple levels of complexities encountered. We have also developed the grid-enabling E3D application code which takes our own dialect XML inputs that include geological models that are accessible through standard Web services. Also, the XML inputs for this application code contain structural geometries, source parameters, seismic velocity, density, attenuation values, number of time steps to compute, and number of stations.In this paper, we emphasize the development of a state-of-the-art web-based scientific computational environment. Our system can be used to promote an efficient and effective modeling environment to help scientists as well as educators in their daily activities and speed up the scientific discovery process.     

Scheduling parameter sweep applications on global Grids: a deadline and budget constrained cost–time optimization algorithm

Computational Grids and peer‐to‐peer (P2P) networks enable the sharing, selection, and aggregation of geographically distributed resources for solving large‐scale problems in science, engineering, and commerce. The management and composition of resources and services for scheduling applications, however, becomes a complex undertaking. We have proposed a computational economy framework for regulating the supply of and demand for resources and allocating them for applications based on the users' quality‐of‐service requirements. The framework requires economy‐driven deadline‐ and budget‐constrained (DBC) scheduling algorithms for allocating resources to application jobs in such a way that the users' requirements are met. In this paper, we propose a new scheduling algorithm, called the DBC cost–time optimization scheduling algorithm, that aims not only to optimize cost, but also time when possible. The performance of the cost–time optimization scheduling algorithm has been evaluated through extensive simulation and empirical studies for deploying parameter sweep applications on global Grids. Copyright © 2005 John Wiley & Sons, Ltd.     

A software tool for semi-automatic gridification of resource-intensive Java bytecodes and its application to ray tracing and sequence alignment

Computational Grids deliver the necessary computational infrastructure to perform resource-intensive computations such as the ones that solve the problems scientists are facing today. Exploiting Computational Grids comes at the expense of explicitly adapting the ordinary software implementing scientific problems to take advantage of Grid resources, which unavoidably requires knowledge on Grid programming. The recent notion of “gridifying” ordinary applications, which is based on semi-automatically deriving a Grid-aware version from the compiled code of a sequential application, promises users to be relieved from the requirement of manual usage of Grid APIs within their source codes. In this paper, we describe a novel gridification tool that allows users to easily parallelize Java applications on Grids. Extensive experiments with two real-world applications - ray tracing and sequence alignment - suggest that our approach provides a convenient balance between ease of gridification and Grid resource exploitation compared to manually using Grid APIs for gridifying ordinary applications.     

CFD网格应用平台研究

CFD网格应用平台是ChinaGrid5大应用平台之一,它为各种不同的CFD应用提供了网格基础设施。平台采用了两层架构：上层基于WSRF服务,下层基于并行计算。为了无缝整合各种CFD应用,平台定义了一组包括CFD工作流模板和CFD分类码的重要概念,使用了一系列如CGNS数据可视化工具GVisual的关键技术。目前基于Service Domain的平台原型系统已经开发完成,性能测试表明CFD应用移植其到平台上后性能获得提高。     

Neuroscience instrumentation and distributed analysis of brain activity data: a case for eScience on global Grids

The distribution of knowledge (by scientists) and data sources (advanced scientific instruments), and the need for large‐scale computational resources for analyzing massive scientific data are two major problems commonly observed in scientific disciplines. Two popular scientific disciplines of this nature are brain science and high‐energy physics. The analysis of brain‐activity data gathered from the MEG (magnetoencephalography) instrument is an important research topic in medical science since it helps doctors in identifying symptoms of diseases. The data needs to be analyzed exhaustively to efficiently diagnose and analyze brain functions and requires access to large‐scale computational resources. The potential platform for solving such resource intensive applications is the Grid. This paper presents the design and development of MEG data analysis system by leveraging Grid technologies, primarily Nimrod‐G, Gridbus, and Globus. It describes the composition of the neuroscience (brain‐activity analysis) application as parameter‐sweep application and its on‐demand deployment on global Grids for distributed execution. The results of economic‐based scheduling of analysis jobs for three different optimizations scenarios on the world‐wide Grid testbed resources are presented along with their graphical visualization. Copyright © 2005 John Wiley & Sons, Ltd.     

HPC on the Grid: The Theophys Experience

The Grid Virtual Organization (VO) “Theophys”, associated to the INFN (Istituto Nazionale di Fisica Nucleare), is a theoretical physics community with various computational demands, spreading from serial, SMP, MPI and hybrid jobs. That has led, in the past 20 years, towards the use of the Grid infrastructure for serial jobs, while the execution of multi-threaded, MPI and hybrid jobs has been performed in several small-medium size clusters installed in different sites, with access through standard local submission methods. This work analyzes the support for parallel jobs in the scientific Grid middlewares, then describes how the community unified the management of most of its computational need (serial and parallel ones) using the Grid through the development of a specific project which integrates serial e parallel resources in a common Grid based framework. A centralized national cluster is deployed inside this framework, providing “Wholenodes” reservations, CPU affinity, and other new features supporting our High Performance Computing (HPC) applications in the Grid environment. Examples of the cluster performance for relevant parallel applications in theoretical physics are reported, focusing on the different kinds of parallel jobs that can be served by the new features introduced in the Grid.     

PadSpace: A new framework for the service federation of web resources

End-users need a simple and interactive tool for service composition development. A PadSpace proposes an extension of a typical Linda-like coordination model (tuplespace) to provide mechanisms for the interoperation among Web applications, Web services and end-users’ local functional resources. First, a PadSpace provides an end-user supporting tool for composing Web applications, Web services, and local visual resources based on the meme media architecture without writing any program codes. It enables end-users to directly manipulate visual components, and to create new composite components for the creation of services that use Web applications, Web services, and local functional resources. Second, a PadSpace provides a spreadsheet-based service-coordination tool for end-users to orchestrate multiple Web applications, Web services, and local functional resources. Finally, we show some new applications of service composition and service orchestration.     

MicroMAIS: executing and orchestrating Web services on constrained mobile devices

Mobile devices with their more and more powerful resources allow the development of mobile information systems in which services are not only provided by traditional systems but also autonomously executed and controlled in the mobile devices themselves. Services distributed on autonomous mobile devices allow both the development of cooperative applications without a back‐end infrastructure and the development of applications blending distributed and centralized services. In this paper, we propose MicroMAIS: an integrated platform for supporting the execution of Web service‐based applications natively on a mobile device. The MicroMAIS platform is composed of mAS and μ‐BPEL. The former allows the execution of a single Web service, whereas the latter permits the orchestration of several Web services according to the WS‐BPEL standard. Copyright © 2011 John Wiley & Sons, Ltd.     

Cost minimization in utility computing systems

Utility computing is a form of computer service whereby the company providing the service charges the users for using the system resources. In this paper, we present system‐optimal and user‐optimal price‐based job allocation schemes for utility computing systems whose objective is to minimize the cost for the users. The system‐optimal scheme provides an allocation of jobs to the computing resources that minimizes the overall cost for executing all the jobs in the system. The user‐optimal scheme provides an allocation that minimizes the cost for individual users in the system for providing fairness. The system‐optimal scheme is formulated as a constraint minimization problem, and the user‐optimal scheme is formulated as a non‐cooperative game. The prices charged by the computing resource owners for executing the users jobs are obtained using a pricing model based on a non‐cooperative bargaining game theory framework. The performance of the studied job allocation schemes is evaluated using simulations with various system loads and configurations. Copyright © 2012 John Wiley & Sons, Ltd.     

Wingrid-面向参数扫描应用的网格计算系统

参数扫描应用在计算网格环境下扮演十分重要的角色。在Wingrid项目中,我们提出并实现了一种面向参数扫描的自适应调度机制。客户端,主节点和从节点的调度基础设施,以及基于领导节点的通信系统能够改善调度的效率。同时,我们比较了自适应workqueue算法和标准启发式调度算法。实验结果显示大网络延迟下,启发式调度算法效率高于workqueue算法,在各种启发式算法中,min-min启发式算法的任务完成时间最小。     

Service level agreement based adaptive Grid superscheduling

Grid computing brings heterogeneity and decentralization to the world of science and technology. It leverages every bit of idle computing resources and provides a straightforward middleware for integrating cross-domain scientific devices and legacy systems. In a super big Grid, job scheduling is challenging specifically when it needs to have access to vast amount of resources. The process of mapping jobs onto Grid resources requires significant consideration in terms of Grid architecture design, consumer demands and provider revenues. In this paper, we simultaneously utilize the legacy architecture of superscheduling, forwarding strategy, service level, success rate, and service pricing strategies and finally propose a service level agreement based on adaptive superscheduling (SAS) algorithm. SAS algorithm presents unified connectivity via efficient diffusion of jobs through the Grid infrastructure that is fueled from the previous scheduling events across the Grid. Moreover, by enforcing the service level agreement terms from a rich set of ask and bid prices, system performance, and load statistics, SAS successfully boosts revenue and utilization statistics. We perform an extensive experimental analysis for different Grid scales. Based on our experimental result, the SAS algorithm maximizes revenue while guarantees quality of service. More specifically, the quality of service is achieved through a high ratio of completed jobs and remarkable utilization of resources.     

基于 Portlet 的高性能计算应用集成组件

高性能计算 Web 应用社区经常需要封装不同类型的应用,封装过程需开发或调整源代码。因此,社区管理员不能快速满足应用封装需求,特别是作业参数相对复杂的应用。通过分析不同作业管理系统的作业提交和管理命令、不同学科领域的高性能计算应用程序的使用方式、通用作业描述语言的结构和语义,本文提出了一种基于 portlet 的高性能计算应用集成组件,设计和实现了应用需求描述模板、作业提交页面动态渲染引擎、作业参数存储和填充方法,从而提供了基于 WEB 页面的应用封装、动态生成作业提交 Web页面和作业描述并提交作业的功能,并且支持以历史作业的输入数据填充作业提交页面的功能。分析及实例表明,该组件能够有效提高应用封装的响应速度,用户能够方便的提交作业和重用复杂的作业参数,具有良好的可移植性和扩展性。     

Performance analysis based resource allocation for green cloud computing

Cloud computing has become a new computing paradigm that has huge potentials in enterprise and business. Green cloud computing is also becoming increasingly important in a world with limited energy resources and an ever-rising demand for more computational power. To maximize utilization and minimize total cost of the cloud computing infrastructure and running applications, resources need to be managed properly and virtual machines shall allocate proper host nodes to perform the computation. In this paper, we propose performance analysis based resource allocation scheme for the efficient allocation of virtual machines on the cloud infrastructure. We experimented the proposed resource allocation algorithm using CloudSim and its performance is compared with two other existing models.     

The Management Service Provider Option

Management service providers (MSPs) are specialized application service providers that manage infrastructure resources or applications. They offer tool implementation and hosting, cost savings, rapid time to value, expertise to supplement staff resources, and an outside perspective.     

Cloud computing paradigms for pleasingly parallel biomedical applications

Cloud computing offers exciting new approaches for scientific computing that leverage major commercial players’ hardware and software investments in large‐scale data centers. Loosely coupled problems are very important in many scientific fields, and with the ongoing move towards data‐intensive computing, they are on the rise. There exist several different approaches to leveraging clouds and cloud‐oriented data processing frameworks to perform pleasingly parallel (also called embarrassingly parallel) computations. In this paper, we present three pleasingly parallel biomedical applications: (i) assembly of genome fragments; (ii) sequence alignment and similarity search; and (iii) dimension reduction in the analysis of chemical structures, which are implemented utilizing a cloud infrastructure service‐based utility computing models of Amazon Web Services ( http://Amazon.com Inc., Seattle, WA, USA) and Microsoft Windows Azure (Microsoft Corp., Redmond, WA, USA) as well as utilizing MapReduce‐based data processing frameworks Apache Hadoop (Apache Software Foundation, Los Angeles, CA, USA) and Microsoft DryadLINQ. We review and compare each of these frameworks, performing a comparative study among them based on performance, cost, and usability. High latency, eventually consistent cloud infrastructure service‐based frameworks that rely on off‐the‐node cloud storage were able to exhibit performance efficiencies and scalability comparable to the MapReduce‐based frameworks with local disk‐based storage for the applications considered. In this paper, we also analyze variations in cost among the different platform choices (e.g., Elastic Compute Cloud instance types), highlighting the importance of selecting an appropriate platform based on the nature of the computation. Copyright © 2011 John Wiley & Sons, Ltd.     

Design of a grid service-based platform for in silico protein-ligand screenings

Grid computing offers the powerful alternative of sharing resources on a worldwide scale, across different institutions to run computationally intensive, scientific applications without the need for a centralized supercomputer. Much effort has been put into development of software that deploys legacy applications on a grid-based infrastructure and efficiently uses available resources. One field that can benefit greatly from the use of grid resources is that of drug discovery since molecular docking simulations are an integral part of the discovery process. In this paper, we present a scalable, reusable platform to choreograph large virtual screening experiments over a computational grid using the molecular docking simulation software DOCK. Software components are applied on multiple levels to create automated workflows consisting of input data delivery, job scheduling, status query, and collection of output to be displayed in a manageable fashion for further analysis. This was achieved using Opal OP to wrap the DOCK application as a grid service and PERL for data manipulation purposes, alleviating the requirement for extensive knowledge of grid infrastructure. With the platform in place, a screening of the ZINC 2,066,906 compound "drug-like" subset database against an enzyme's catalytic site was successfully performed using the MPI version of DOCK 5.4 on the PRAGMA grid testbed. The screening required 11.56 days laboratory time and utilized 200 processors over 7 clusters.     

SAaaS: a cloud computing service model using semantic‐based agent

Cloud computing, a common business model, provides cloud resources on demand to consumers over the Internet. However, because cloud computing lacks a uniform method of representing knowledge, which can offer customers a comprehensive solution for managing and developing cloud applications, cloud computing has low reuse potential. This work proposes a Semantic Agent as a Service (SAaaS), which was developed using Unified Modeling Language modelling. The SAaaS architecture is based on research into Cloud Computing, Semantic Web and Multi‐Agent Systems. The architecture can be combined with existing cloud service models, such as Software as a Service, Platform as a Service and Infrastructure as a Service, to design intelligent cloud computing applications. To demonstrate the efficacy of SAaaS, a Semantic‐based Project Resources Sharing Platform, an intelligent cloud computing application based on the SAaaS framework, is implemented to provide project resources on demand, consistent with the needs of project members.     

Web Service在工业过程监控系统中的应用

研究了Web Service,XML,SOAP,WSDL,UDDI等相关网络技术,以及基于XML Web Service服务架构技术在过程监控领域中的应用。由于采用了web Service技术构建实时工业过程监控服务器,实现了监控平台跨越应用系统的对象体系、运行平台、开发语言等界限,以服务的形式封装应用并对外发布,提供用户或其他平台调用,从而形成了基于Web的服务的监控共享平台。以实际例子介绍了Web Service应用的具体方案。