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 computational economy for grid computing and its implementation in the Nimrod-G resource broker

Computational grids that couple geographically distributed resources such as PCs, workstations, clusters, and scientific instruments, have emerged as a next generation computing platform for solving large-scale problems in science, engineering, and commerce. However, application development, resource management, and scheduling in these environments continue to be a complex undertaking. In this article, we discuss our efforts in developing a resource management system for scheduling computations on resources distributed across the world with varying quality of service (QoS). Our service-oriented grid computing system called Nimrod-G manages all operations associated with remote execution including resource discovery, trading, scheduling based on economic principles and a user-defined QoS requirement. The Nimrod-G resource broker is implemented by leveraging existing technologies such as Globus, and provides new services that are essential for constructing industrial-strength grids. We present the results of experiments using the Nimrod-G resource broker for scheduling parametric computations on the World Wide Grid (WWG) resources that span five continents.     

基于经济模型的资源代理

网格计算是为解决大规模资源密集型问题而提出的新一代计算平台,是当前并行和分布处理技术的一个发展方向,而资源管理是计算网格的关键技术之一。对各种各样可利用资源的整合和管理是网格应用的基础,而资源的分布性、动态性、异构性、自治性和需要协调一致性使得网格资源的管理调度成为一个棘手的问题。目前基于市场的经济资源管理和调度算法非常适合计算网格中的资源管理问题,但有调度价格不能更改、负载平衡等问题。文中提出了“网格环境下基于经济模型的资源代理”,依靠多维QoS指导的调度策略和经济模型的启发式调节资源价格,改进和优化计算网格资源的分配。     

Network Bandwidth-aware job scheduling with dynamic information model for Grid resource brokers

A resource broker with a user-friendly interface for job submission developed on a platform constructed using the Globus toolkit is proposed. The broker employs a domain-based network information model and dynamic version to measure network statuses, and also monitors and collects resource statuses and network-related information as the basis of its brokerage. A network bandwidth-aware job scheduling algorithm for brokering suitable Grid resources to communication-intensive jobs based on improving and preserving the advantages of our previously developed network information model is also proposed. Using timely information, the resource broker effectively matches Grid resources and user requests, thus improving job execution efficiency.     

Grid resource brokering algorithms enabling advance reservations and resource selection based on performance predictions

We present algorithms, methods, and software for a Grid resource manager, that performs resource brokering and job scheduling in production Grids. This decentralized broker selects computational resources based on actual job requirements, job characteristics, and information provided by the resources, with the aim to minimize the total time to delivery for the individual application. The total time to delivery includes the time for program execution, batch queue waiting, and transfer of executable and input/output data to and from the resource. The main features of the resource broker include two alternative approaches to advance reservations, resource selection algorithms based on computer benchmark results and network performance predictions, and a basic adaptation facility. The broker is implemented as a built-in component of a job submission client for the NorduGrid/ARC middleware.     

REMUS: A Rerouting and Multiplexing System for Grid Connectivity Across Firewalls

The Grid provides unique opportunities for high-performance computing through distributed applications that execute over multiple remote resources. Participating institutions can form a virtual organization to maximize the utilization of collective resources as well as to facilitate collaborative projects. However, there are two design aspects in distributed environments like the Grid that can easily clash: security and resource sharing. It may be that resources are secure but are not entirely conducive to resource sharing, or networks are wide open for resource sharing but sacrifice security as a result. We developed REMUS, a rerouting and multiplexing system that provides a compromise through connection rerouting and wrappers. REMUS reroutes connections using proxies, ports and protocols that are already authorized across firewalls, avoiding the need to make new openings through the firewalls. We also encapsulate applications within wrappers, transparently rerouting the connections among Grid applications without modifying their programs. In this paper, we describe REMUS and the tests we conducted across firewalls using two Grid middleware case studies: Globus Toolkit 2.4 and Nimrod/G 3.0.     

A multicriteria approach to two-level hierarchy scheduling in grids

In this paper we address a multicriteria scheduling problem for computational Grid systems. We focus on the two-level hierarchical Grid scheduling problem, in which at the first level (the Grid level) a Grid broker makes scheduling decisions and allocates jobs to Grid nodes. Jobs are then sent to the Grid nodes, where local schedulers generate local schedules for each node accordingly. A general approach is presented taking into account preferences of all the stakeholders of Grid scheduling (end-users, Grid administrators, and local resource providers) and assuming a lack of knowledge about job time characteristics. A single-stakeholder, single-criterion version of the approach has been compared experimentally with the existing approaches.     

Ontology‐based Grid resource management

Grid resources are typically diverse in nature with respect to their software and hardware configurations, resource usage policies and the kind of application they support. Aggregating and monitoring these resources, and discovering suitable resources for the applications become a challenging issue. This is partially due to the representation of Grid metadata supported by the existing Grid middleware which offers limited scope for matching the job requirements that directly affect scheduling decisions. This paper proposes a semantic component in conventional Grid architecture to support ontology‐based representation of Grid metadata and facilitate context‐based information retrieval that complements Grid schedulers for effective resource management. Web Ontology language is used for creating Grid resource ontology and Algernon inference engine has been used for resource discovery. This semantic component has been integrated with conventional Grid schedulers. Several experiments have also been carried out to investigate the performance overhead that arises while integrating this component with Grid schedulers. Copyright © 2009 John Wiley & Sons, Ltd.     

基于缓冲和预选的网格资源调度优化

资源发现是影响调度效率的关键,针对网格资源发现中复杂的查询过程,通过资源代理,将用户任务发生过调度的资源在本地进行缓存,构成黑白资源名单。同时,在网格资源代理空闲时,按照最近调度的QoS参数为参考值,进行资源的预选择,存放在本地记录,由此构成了本地记录优先的资源发现。针对该方法,设计了支撑该方法的优化代理体系结构,讨论了记录管理问题,给出了优化资源调度过程的算法。实验表明,该方法缩短了任务的平均调度时间,从而缩短了任务平均完成时间,提高了吞吐量,达到了优化的目的。     

Resource brokering using a multi‐site resource allocation strategy for computational grids

Grid computing employs heterogeneous resources which may be installed on different platforms, hardware/software, computer architectures, and perhaps using different computer languages to solve large‐scale computational problems. As many more Grids are being developed worldwide, the number of multi‐institutional collaborations is growing rapidly. However, to realize Grid computing's full potential, it is expected that Grid participants must be able to share one another's resources. This paper presents a resource broker that employs the multi‐site resource allocation (MSRA) strategy and the dynamic domain‐based network information model that we propose to allocate Grid resources to submitted jobs, where the Grid resources may be dispersed at different sites, and owned and governed by different organizations or institutes. The jobs and resources may also belong to different clusters/sites. Resource statuses collected by the Ganglia, and network bandwidths gathered by the Network Weather Service, are both considered in the proposed scheduling approach. A dynamic domain‐based model for network information measurement is also proposed to choose the most appropriate resources that meet the jobs' execution requirements. Experimental results show that MSRA outperformed the other tested strategies. Copyright © 2010 John Wiley & Sons, Ltd.     

Survey on Grid Resource Allocation Mechanisms

Grid is a distributed high performance computing paradigm that offers various types of resources (like computing, storage, communication) to resource-intensive user tasks. These tasks are scheduled to allocate available Grid resources efficiently to achieve high system throughput and to satisfy user requirements. The task scheduling problem has become more complex with the ever increasing size of Grid systems. Even though selecting an efficient resource allocation strategy for a particular task helps in obtaining a desired level of service, researchers still face difficulties in choosing a suitable technique from a plethora of existing methods in literature. In this paper, we explore and discuss existing resource allocation mechanisms for resource allocation problems employed in Grid systems. The work comprehensively surveys Gird resource allocation mechanisms for different architectures (centralized, distributed, static or dynamic). The paper also compares these resource allocation mechanisms based on their common features such as time complexity, searching mechanism, allocation strategy, optimality, operational environment and objective function they adopt for solving computing- and data-intensive applications. The comprehensive analysis of cutting-edge research in the Grid domain presented in this work provides readers with an understanding of essential concepts of resource allocation mechanisms in Grid systems and helps them identify important and outstanding issues for further investigation. It also helps readers to choose the most appropriate mechanism for a given system/application.     

Design and implementation of a workflow-based resource broker with information system on computational grids

The grid is a promising infrastructure that can allow scientists and engineers to access resources among geographically distributed environments. Grid computing is a new technology which focuses on aggregating resources (e.g., processor cycles, disk storage, and contents) from a large-scale computing platform. Making grid computing a reality requires a resource broker to manage and monitor available resources. This paper presents a workflow-based resource broker whose main functions are matching available resources with user requests and considering network information statuses during matchmaking in computational grids. The resource broker provides a graphic user interface for accessing available and the appropriate resources via user credentials. This broker uses the Ganglia and NWS tools to monitor resource status and network-related information, respectively. Then we propose a history-based execution time estimation model to predict the execution time of parallel applications, according to previous execution results. The experimental results show that our model can accurately predict the execution time of embarrassingly parallel applications. We also report on using the Globus Toolkit to construct a grid platform called the TIGER project that integrates resources distributed across five universities in Taichung city, Taiwan, where the resource broker was developed.

基于计算经济模型改善网格资源分发和发现的性能

该文在基于计算经济的计算网格体系结构Nimrod/G的基础上,结合NWS设计并实现了一个网格资源分发和发现原型系统,实验表明:该系统可以有效地改进网格系统中的资源分发和发现的性能,进一步提高了网格资源的管理和调度的性能。     

Designing a resource broker for heterogeneous grids

Grids provide uniform access to aggregations of heterogeneous resources and services such as computers, networks and storage owned by multiple organizations. However, such a dynamic environment poses many challenges for application composition and deployment. In this paper, we present the design of the Gridbus Grid resource broker that allows users to create applications and specify different objectives through different interfaces without having to deal with the complexity of Grid infrastructure. We present the unique requirements that motivated our design and discuss how these provide flexibility in extending the functionality of the broker to support different low‐level middlewares and user interfaces. We evaluate the broker with different job profiles and Grid middleware and conclude with the lessons learnt from our development experience. Copyright © 2007 John Wiley & Sons, Ltd.     

Reputation-based dependable scheduling of workflow applications in Peer-to-Peer Grids

Grids facilitate creation of wide-area collaborative environment for sharing computing or storage resources and various applications. Inter-connecting distributed Grid sites through peer-to-peer routing and information dissemination structure (also known as Peer-to-Peer Grids) is essential to avoid the problems of scheduling efficiency bottleneck and single point of failure in the centralized or hierarchical scheduling approaches. On the other hand, uncertainty and unreliability are facts in distributed infrastructures such as Peer-to-Peer Grids, which are triggered by multiple factors including scale, dynamism, failures, and incomplete global knowledge.In this paper, a reputation-based Grid workflow scheduling technique is proposed to counter the effect of inherent unreliability and temporal characteristics of computing resources in large scale, decentralized Peer-to-Peer Grid environments. The proposed approach builds upon structured peer-to-peer indexing and networking techniques to create a scalable wide-area overlay of Grid sites for supporting dependable scheduling of applications. The scheduling algorithm considers reliability of a Grid resource as a statistical property, which is globally computed in the decentralized Grid overlay based on dynamic feedbacks or reputation scores assigned by individual service consumers mediated via Grid resource brokers. The proposed algorithm dynamically adapts to changing resource conditions and offers significant performance gains as compared to traditional approaches in the event of unsuccessful job execution or resource failure. The results evaluated through an extensive trace driven simulation show that our scheduling technique can reduce the makespan up to 50% and successfully isolate the failure-prone resources from the system.     

MOLECULAR DOCKING: An example of grid enabled applications

The molecular docking web interface was developed to execute Autodock3.05 molecular docking program in the Grid environment. The nature of the application which allows the whole docking jobs to be broken up into multiple small independent tasks, has the potential of utilizing the availability of the Grid computing. Using the web interface, the whole docking procedures can be automated from the start to the end. Automation includes the preparation of the target receptor, creation of parameter files (gpf and dpf), calculation of grid energy, and docking of molecules. Once the job is split into small tasks, the tasks are submitted to Globus GRAM that submits the tasks to the resources available in the Grid environment. The execution of the grid-enabled AutoDock 3.05 is tested and the results showed that the process of molecular docking are faster compared if the execution is run on sequential computing resources.     

Dynamic workload balancing of parallel applications with user-level scheduling on the Grid

This paper suggests a hybrid resource management approach for efficient parallel distributed computing on the Grid. It operates on both application and system levels, combining user-level job scheduling with dynamic workload balancing algorithm that automatically adapts a parallel application to the heterogeneous resources, based on the actual resource parameters and estimated requirements of the application. The hybrid environment and the algorithm for automated load balancing are described, the influence of resource heterogeneity level is measured, and the speedup achieved with this technique is demonstrated for different types of applications and resources.     

A hierarchical reliability-driven scheduling algorithm in grid systems

In a Grid computing system, many distributed scientific and engineering applications often require multi-institutional collaboration, large-scale resource sharing, wide-area communication, etc. Applications executing in such systems inevitably encounter different types of failures such as hardware failure, program failure, and storage failure. One way of taking failures into account is to employ a reliable scheduling algorithm. However, most existing Grid scheduling algorithms do not adequately consider the reliability requirements of an application. In recognition of this problem, we design a hierarchical reliability-driven scheduling architecture that includes both a local scheduler and a global scheduler. The local scheduler aims to effectively measure task reliability of an application in a Grid virtual node and incorporate the precedence constrained tasks’ reliability overhead into a heuristic scheduling algorithm. In the global scheduler, we propose a hierarchical reliability-driven scheduling algorithm based on quantitative evaluation of independent application reliability. Our experiments, based on both randomly generated graphs and the graphs of some real applications, show that our hierarchical scheduling algorithm performs much better than the existing scheduling algorithms in terms of system reliability, schedule length, and speedup.     

QoS based resource provisioning and scheduling in grids

As Grid computing has emerged as a technology for providing the computational resources to industries and scientific projects, new requirements arise. Nowadays, resource management has become an important research area in the Grid computing environment. To provision the appropriate resource to a corresponding application is a tedious task. So, it is important to check and verify the provisioning of the resource before the application’s execution. In this paper, a resource provisioning framework has been presented that offers a resource provisioning policy, which caters to provisioned resource allocation and resource scheduling. The framework has been formally specified and verified. Formal specification and verification of the framework helps in predicting possible errors before the scheduling process itself, and thus results in efficient resource provisioning and scheduling of Grid resources.     

The DataGrid Workload Management System: Challenges and Results

The workload management task of the DataGrid project was mandated to define and implement a suitable architecture for distributed scheduling and resource management in a Grid environment. The result was the design and implementation of a Grid Workload Management System, a super-scheduler with the distinguishing property of being able to take data access requirements into account when scheduling jobs to the available Grid resources. Many novel issues in various fields were faced such as resource management, resource reservation and co-allocation, Grid accounting. In this paper, the architecture and the functionality provided by the DataGrid Workload Management System are presented.