Multi-Grid, Multi-User Workflows in the P-GRADE Grid Portal

Computational Grids connect resources and users in a complex way in order to deliver nontrivial qualities of services. According to the current trend various communities build their own Grids and due to the lack of generally accepted standards these Grids are usually not interoperable. As a result, large scale sharing of resources is prevented by the isolation of Grid systems. Similarly, people are isolated, because the collaborative work of Grid users is not supported by current environments. Each user accesses Grids as an individual person without having the possibility of organizing teams that could overcome the difficulties of application development and execution more easily. The paper describes a new workflow-oriented portal concept that solves both problems. It enables the interoperability of various Grids during the execution of workflow applications, and supports users to develop and run their Grid workflows in a collaborative way. The paper also introduces a classification model that can be used to identify workflow-oriented Grid portals based on two general features: Ability to access multiple Grids, and support for collaborative problem solving. Using the approach the different potential portal types are introduced, their unique features are discussed and the portals and Problem Solving Environments (PSE) of our days are classified. The P-GRADE Portal as a Globus-based implementation for the classification model is also presented. The work described in this paper is supported by the Hungarian Grid project (IHM 4671/1/2003), by the Hungarian OTKA project (No. T042459) and a collaboration project with the University of Reading.     

The Weka4WS framework for distributed data mining in service‐oriented Grids

The service‐oriented architecture paradigm can be exploited for the implementation of data and knowledge‐based applications in distributed environments. The Web services resource framework (WSRF) has recently emerged as the standard for the implementation of Grid services and applications. WSRF can be exploited for developing high‐level services for distributed data mining applications. This paper describes Weka4WS, a framework that extends the widely used open source Weka toolkit to support distributed data mining on WSRF‐enabled Grids. Weka4WS adopts the WSRF technology for running remote data mining algorithms and managing distributed computations. The Weka4WS user interface supports the execution of both local and remote data mining tasks. On every computing node, a WSRF‐compliant Web service is used to expose all the data mining algorithms provided by the Weka library. The paper describes the design and implementation of Weka4WS using the WSRF libraries and services provided by Globus Toolkit 4. A performance analysis of Weka4WS for executing distributed data mining tasks in different network scenarios is presented. Copyright © 2008 John Wiley & Sons, Ltd.     

Visual Grid Workflow in Triana

In this paper, we describe the graphical abstractions for Grids and services that have been implemented within the Triana problem solving environment. We provide an overview of the ways in which Triana interacts with services (e.g., Web and P2P services) and then how we interact with core Grid components, such as resource managers and data management systems through the extensive use of the GridLab GAT interface. We describe in detail the GAT philosophy and implementation and then show how the various GAT primitives can be represented in an intuitive fashion within a Triana workflow. This approach, which we refer to as the Visual GAT, differs substantially from other approaches because we do not tie our implementation to any specific underlying Grid middleware technologies; rather, we base our implementation on application level requirements and model such primitives from a user’s perspective by hiding as much complexity as possible without undermining the core capabilities required. We provide a use case to demonstrate the Visual GAT implementation and show how legacy applications can seamlessly be distributed and integrated in a dynamic fashion within complex data-driven workflow scenarios.     

Scalable dimensioning of resilient Lambda Grids

Grids consist of the aggregation of numerous dispersed computational, storage and network resources, able to satisfy even the most demanding computing jobs. Due to the data-intensive nature of Grid jobs, there is an increasing interest in Grids using optical transport networks as this technology allows for the timely delivery of large amounts of data. Such Grids are commonly referred to as Lambda Grids.

An important aspect of Grid deployment is the allocation and activation of installed network capacity, needed to transfer data and jobs to and from remote resources. However, the exact nature of a Grid’s network traffic depends on the way arriving workload is scheduled over the various Grid sites. As Grids possibly feature high numbers of resources, jobs and users, solving the combined Grid network dimensioning and workload scheduling problem requires the use of scalable mathematical methods such as Divisible Load Theory (DLT). Lambda Grids feature additional complexity such as wavelength granularity and continuity or conversion constraints must be enforced. Additionally, Grid resources cannot be expected to be available at all times. Therefore, the extra complexity of resilience against possible resource failures must be taken into account when modelling the combined Grid network dimensioning and workload scheduling problem, enforcing the need for scalable solution methods. In this work, we tackle the Lambda Grid combined dimensioning and workload scheduling problem and incorporate single-resource failure or unavailability scenarios. We use Divisible Load Theory to tackle the scalability problem and compare non-resilient lambda Grid dimensioning to the dimensions needed to survive single-resource failures. We distinguish three failure scenarios relevant to lambda Grid deployment: computational element, network link and optical cross-connect failure. Using regular network topologies, we derive analytical bounds on the dimensioning cost. To validate these bounds, we present comparisons for the resulting Grid dimensions assuming a 2-tier Grid operation as a function of varying wavelength granularity, fiber/wavelength cost models, traffic demand asymmetry and Grid scheduling strategy for a specific set of optical transport networks.     

Grid Economics: A Selective Discussion of Two Research Problems

The last 5 years have seen considerable discussion of various types of Grids—compute Grids, storage Grids, and data Grids. Using the checklist given in Foster (, 2002) to define a Grid, two important problems that arise in the context of resource sharing in Grid computing environments are discussed. First, the well documented problem in compute Grid environments that arises from the inability of consumers to accurately estimate their resource requirements is presented. This results in incorrect scheduling of requests for Grid resources and social welfare loss. To address this problem, two research proposals are briefly described. The first approach argues for the design of decision support tools to help users with resource estimation while the second approach studies the design of resource allocation mechanisms that can work with stochastic specifications of resource requirements. This is in contrast to the traditional point estimates of resource required by extant mechanisms. Next, resource provisioning and pricing problems that arise in data storage and retrieval Grids are described. These Grids differ fundamentally from compute Grids but share some economic characteristics with P2P file sharing networks. Drawing on this connection, pricing mechanisms and resource provisioning research is briefly discussed.     

Modeling and Supporting Grid Scheduling

Grid resource management systems and schedulers are important components for building Grids. They are responsible for the selection and allocation of Grid resources to current and future applications. Thus, they are important building blocks for making Grids available to user communities. In this paper we briefly analyze the requirements of Grid resource management and provide a classification of schedulers. Then, we define an extensible formal model for Grid scheduling activities, and characterize the general Grid scheduling problem. Finally, we provide a reference architecture for the support of our model and discuss different aspects of architectural implementations.     

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.     

A Taxonomy of Workflow Management Systems for Grid Computing

With the advent of Grid and application technologies, scientists and engineers are building more and more complex applications to manage and process large data sets, and execute scientific experiments on distributed resources. Such application scenarios require means for composing and executing complex workflows. Therefore, many efforts have been made towards the development of workflow management systems for Grid computing. In this paper, we propose a taxonomy that characterizes and classifies various approaches for building and executing workflows on Grids. We also survey several representative Grid workflow systems developed by various projects world-wide to demonstrate the comprehensiveness of the taxonomy. The taxonomy not only highlights the design and engineering similarities and differences of state-of-the-art in Grid workflow systems, but also identifies the areas that need further research.     

P2P-MPI: A Peer-to-Peer Framework for Robust Execution of Message Passing Parallel Programs on Grids

This paper presents P2P-MPI, a middleware aimed at computational Grids. From the programmer point of view, P2P-MPI provides a message-passing programming model which enables the development of MPI applications for Grids. Its originality lies in its adaptation to unstable environments. First, the peer-to-peer design of P2P-MPI allows for a dynamic discovery of collaborating resources. Second, it gives the user the possibility to adjust the robustness of an execution thanks to an internal process replication mechanism. Finally, we measure the performance of the integrated message passing library on several benchmarks and on different hardware platforms.     

A high level data structure—The grid

Grids are arrays that can have any shape: grid elements need not be connected to each other (e.g., a grid may be pyramid-like or two disjoint rectangular pieces). Programs using grids are smaller, semantically clearer, more general and easier to modify than programs that simulate non array-like shapes. We present a notation for specifying grids in PASCAL. Grids have been implemented as an extension to FORTRAN.     

Achieving efficiency, quality of service and robustness in multi-organizational Grids

Scalability, flexibility, quality of service provisioning, efficiency and robustness are the desired characteristics of most computing systems. Although the emerging Grid computing paradigm is scalable and flexible, achieving both efficiency and quality of service provisioning in Grids is a challenging task but is necessary for the wide adoption of Grids. Grid middleware should also be robust to uncertainties such as those in user-estimated runtimes of Grid applications. In this paper, we present a complete middleware framework for Grids that achieves user satisfaction by providing QoS guarantees for Grid applications, cost effectiveness by efficiently utilizing resources and robustness by intelligently handling uncertain runtimes of applications.     

Grid today, clouds on the horizon

By the time of CCP 2008, the largest scientific machine in the world - the Large Hadron Collider - had been cooled down as scheduled to its operational temperature of below 2 degrees Kelvin and injection tests were starting. Collisions of proton beams at 5+5 TeV were expected within one to two months of the initial tests, with data taking at design energy (7+7 TeV) foreseen for 2009.In order to process the data from this world machine, we have put our “Higgs in one basket” - that of Grid computing [The Worldwide LHC Computing Grid (WLCG), in: Proceedings of the Conference on Computational Physics 2006 (CCP 2006), vol. 177, 2007, pp. 219-223]. After many years of preparation, 2008 saw a final “Common Computing Readiness Challenge” (CCRC'08) - aimed at demonstrating full readiness for 2008 data taking, processing and analysis. By definition, this relied on a world-wide production Grid infrastructure.But change - as always - is on the horizon. The current funding model for Grids - which in Europe has been through 3 generations of EGEE projects, together with related projects in other parts of the world, including South America - is evolving towards a long-term, sustainable e-infrastructure, like the European Grid Initiative (EGI) [The European Grid Initiative Design Study, website at http://web.eu-egi.eu/]. At the same time, potentially new paradigms, such as that of “Cloud Computing” are emerging.This paper summarizes the results of CCRC'08 and discusses the potential impact of future Grid funding on both regional and international application communities. It contrasts Grid and Cloud computing models from both technical and sociological points of view. Finally, it discusses the requirements from production application communities, in terms of stability and continuity in the medium to long term.     

An effective and robust two-phase resource allocation scheme for interdependent tasks in mobile ad hoc computational Grids

This paper addresses the problem of resource allocation to interdependent tasks in mobile ad hoc computational Grids. Dependencies between tasks imply that there can be heavy communication induced by data transfers between tasks executed on separate nodes. The communication in mobile ad hoc Grids is always expensive and unreliable, and therefore plays a critical role in application performance. There are several factors that contribute to communication cost. Unreliable and short-term connectivity can increase communication cost due to frequent failure and activation of links, and ineffective resource allocation can increase communication cost due to multi hop communication between dependent tasks. To reduce communication cost, an effective and robust resource allocation scheme is required. However, the design of such a scheme for mobile ad hoc computational Grids exhibits numerous difficulties due to the constrained communication environment, node mobility, and lack of pre-existing network infrastructure.     

Grid programming: some indications where we are headed

Grid computing enables the development of large scientific applications on an unprecedented scale. Grid-aware applications, also called meta-applications or multi-disciplinary applications, make use of coupled computational resources that are not available at a single site. In this light, the Grids let scientists solve larger or new problems by pooling together resources that could not be coupled easily before. It is well known that the programmer’s productivity in designing and implementing efficient distributed/parallel applications on high-performance computers is still usually a very time-consuming task. Grid computing makes the situation worse. Consequently, the development of Grid programming environments that would enable programmers to efficiently exploit this technology is an important and hot research issue.

After an introduction on the main Grid programming issues, this paper will review the most important approaches/projects conducted in this field worldwide.     

聚类分析方法在网格技术中的应用

建立在网格基础上的数据挖掘结合了网格技术的优点,能够对各种信息载体中分布的海量信息进行高效的处理、分析和挖掘。本文分析了网格技术与聚类分析方法的特点,使用基于网格方法的数据分析方法将空间划分为由（超）矩形网格单元组成的网格,然后在网格单元上进行聚类分析,以达到更加深入的进行数据挖掘分析之目的,     

Self-adjustment of resource allocation for grid applications

Grids involve coordinated resource sharing and problem solving in heterogeneous dynamic environments to meet the needs of a generation of researchers requiring large amounts of bandwidth and more powerful computational resources. The lack of resource ownership by grid schedulers and fluctuations in resource availability require mechanisms which will enable grids to adjust themselves to cope with fluctuations. The lack of a central controller implies a need for self-adaptation. Grids must thus be enabled with the ability to discover, monitor and manage the use of resources so they can operate autonomously. Two different approaches have been conceived to match the resource demands of grid applications to resource availability: Dynamic scheduling and adaptive scheduling. However, these two approaches fail to address at least one of three important issues: (i) the production of feasible schedules in a reasonable amount of time in relation to that required for the execution of an application; (ii) the impact of network link availability on the execution time of an application; and (iii) the necessity of migrating codes to decrease the execution time of an application. To overcome these challenges, this paper proposes a procedure for enabling grid applications, composed of various dependent tasks, to deal with the availability of hosts and links bandwidth. This procedure involves task scheduling, resource monitoring and task migration, with the goal of decreasing the execution time of grid applications. The procedure differs from other approaches in the literature because it constantly considers changes in resource availability, especially network bandwidth availability, to trigger task migration. The proposed procedure is illustrated via simulation using various scenarios involving fluctuation of resource availability. An additional contribution of this paper is the introduction of a set of schedulers offering solutions which differ in terms of both schedule length and computational complexity. The distinguishing aspect of this set of schedulers is the consideration of time requirements in the production of feasible schedules. Performance is then evaluated considering various network topologies and task dependencies.     

GridBLAST: a Globus‐based high‐throughput implementation of BLAST in a Grid computing framework

Improvements in the performance of processors and networks have made it feasible to treat collections of workstations, servers, clusters and supercomputers as integrated computing resources or Grids. However, the very heterogeneity that is the strength of computational and data Grids can also make application development for such an environment extremely difficult. Application development in a Grid computing environment faces significant challenges in the form of problem granularity, latency and bandwidth issues as well as job scheduling. Currently existing Grid technologies limit the development of Grid applications to certain classes, namely, embarrassingly parallel, hierarchical parallelism, work flow and database applications. Of all these classes, embarrassingly parallel applications are the easiest to develop in a Grid computing framework. The work presented here deals with creating a Grid‐enabled, high‐throughput, standalone version of a bioinformatics application, BLAST, using Globus as the Grid middleware. BLAST is a sequence alignment and search technique that is embarrassingly parallel in nature and thus amenable to adaptation to a Grid environment. A detailed methodology for creating the Grid‐enabled application is presented, which can be used as a template for the development of similar applications. The application has been tested on a ‘mini‐Grid’ testbed and the results presented here show that for large problem sizes, a distributed, Grid‐enabled version can help in significantly reducing execution times. Copyright © 2005 John Wiley & Sons, Ltd.     

关于网格及其它分布计算技术的若干问题的讨论

1.引言在“网格:面向虚拟组织的资源共享技术”一文中,我们主要给出了由Ian Foster等定义的网格及相关基本概念和研究领域,讨论了网格的基本理念和关键技术。在“网格体系结构详解”一文中,详述了Globus项目提出的网格体系结构的构成及功能。这些内容旨在说明网格是什么。实际上,我们也可以从另一方面,或不同的角度来观察和认识网格。比     

基于多代理的网格任务调度研究

随着网格技术的发展,代理技术近年来被用在网格的实现当中。多代理技术用分布式自主结构代替集中式的非自主性结构,具有更强的实时性,特别适合于动态调度,本文提出的网格任务调度系统就采用了多代理的体系结构。由于GLObus已经提供了网格操作系统的大部分功能,本系统构建在Globus之上。在充分利用底层Globus提供的功能并与之结合的基础上,在代理中加入人工智能、知识学习的方法和服务质量实现策略,对网格任务进行灵活智能的调度,实现更好的负载平衡并达到一定的服务质量。采用JATLite来创建该系统,利用该系统可以根据应用背景需求快速定制动态任务调度平台。