2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing最新文献_第10页

SAGA BigJob: An Extensible and Interoperable Pilot-Job Abstraction for Distributed Applications and Systems SAGA BigJob:分布式应用和系统的可扩展和可互操作的试点工作抽象

2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing Pub Date : 2010-05-17 DOI: 10.1109/CCGRID.2010.91

André Luckow, Lukasz Lacinski, S. Jha

{"title":"SAGA BigJob: An Extensible and Interoperable Pilot-Job Abstraction for Distributed Applications and Systems","authors":"André Luckow, Lukasz Lacinski, S. Jha","doi":"10.1109/CCGRID.2010.91","DOIUrl":"https://doi.org/10.1109/CCGRID.2010.91","url":null,"abstract":"The uptake of distributed infrastructures by scientific applications has been limited by the availability of extensible, pervasive and simple-to-use abstractions which are required at multiple levels -- development, deployment and execution stages of scientific applications. The Pilot-Job abstraction has been shown to be an effective abstraction to address many requirements of scientific applications. Specifically, Pilot-Jobs support the decoupling of workload submission from resource assignment, this results in a flexible execution model, which in turn enables the distributed scale-out of applications on multiple and possibly heterogeneous resources. Most Pilot-Job implementations however, are tied to a specific infrastructure. In this paper, we describe the design and implementation of a SAGA-based Pilot-Job, which supports a wide range of application types, and is usable over a broad range of infrastructures, i.e., it is general-purpose and extensible, and as we will argue is also interoperable with Clouds. We discuss how the SAGA-based Pilot-Job is used for different application types and supports the concurrent usage across multiple heterogeneous distributed infrastructure, including concurrent usage across Clouds and traditional Grids/Clusters. Further, we show how Pilot-Jobs can help to support dynamic execution models and thus, introduce new opportunities for distributed applications. We also demonstrate for the first time that we are aware of, the use of multiple Pilot-Job implementations to solve the same problem, specifically, we use the SAGA-based Pilot-Job on high-end resources such as the TeraGrid and the native Condor Pilot-Job (Glide-in) on Condor resources. Importantly both are invoked via the same interface without changes at the development or deployment level, but only an execution (run-time) decision.","PeriodicalId":444485,"journal":{"name":"2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123193505","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 88

A Fair Decentralized Scheduler for Bag-of-Tasks Applications on Desktop Grids 一个公平的分布式调度程序，用于桌面网格上的任务袋应用程序

2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing Pub Date : 2010-05-17 DOI: 10.1109/CCGRID.2010.13

Javier Celaya, L. Marchal

引用次数: 26

Efficient Runtime Environment for Coupled Multi-physics Simulations: Dynamic Resource Allocation and Load-Balancing 耦合多物理场仿真的高效运行环境:动态资源分配和负载平衡

2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing Pub Date : 2010-05-17 DOI: 10.1109/CCGRID.2010.107

S. Ko, Nayong Kim, Joohyun Kim, A. Thota, S. Jha

{"title":"Efficient Runtime Environment for Coupled Multi-physics Simulations: Dynamic Resource Allocation and Load-Balancing","authors":"S. Ko, Nayong Kim, Joohyun Kim, A. Thota, S. Jha","doi":"10.1109/CCGRID.2010.107","DOIUrl":"https://doi.org/10.1109/CCGRID.2010.107","url":null,"abstract":"Coupled Multi-Physics simulations, such as hybrid CFD-MD simulations, represent an increasingly important class of scientific applications. Often the physical problems of interest demand the use of high-end computers, such as TeraGrid resources, which are often accessible only via batch-queues. Batch-queue systems are not developed to natively support the coordinated scheduling of jobs – which in turn is required to support the concurrent execution required by coupled multi-physics simulations. In this paper we develop and demonstrate a novel approach to overcome the lack of native support for coordinated job submission requirement associated with coupled runs. We establish the performance advantages arising from our solution, which is a generalization of the Pilot-Job concept – which in of itself is not new, but is being applied to coupled simulations for the first time. Our solution not only overcomes the initial co-scheduling problem, but also provides a dynamic resource allocation mechanism. Support for such dynamic resources is critical for a load balancing mechanism, which we develop and demonstrate to be effective at reducing the total time-to-solution of the problem. We establish that the performance advantage of using Big Jobs is invariant with the size of the machine as well as the size of the physical model under investigation. The Pilot-Job abstraction is developed using SAGA, which provides an infrastructure agnostic implementation, and which can seamlessly execute and utilize distributed resources.","PeriodicalId":444485,"journal":{"name":"2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124977535","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 33

An Effective Architecture for Automated Appliance Management System Applying Ontology-Based Cloud Discovery 应用基于本体的云发现的自动化设备管理系统的有效架构

2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing Pub Date : 2010-05-17 DOI: 10.1109/CCGRID.2010.87

A. V. Dastjerdi, Sayed Gholam Hassan Tabatabaei, R. Buyya

引用次数: 112

Dynamic Resource Pricing on Federated Clouds 联邦云的动态资源定价

2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing Pub Date : 2010-05-17 DOI: 10.1109/CCGRID.2010.123

Marian Mihailescu, Y. M. Teo

引用次数: 214

In Search of Visualization Metaphors for PlanetLab 为PlanetLab寻找可视化隐喻

2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing Pub Date : 2010-05-17 DOI: 10.1109/CCGRID.2010.35

Andrew J. Zaliwski

引用次数: 2

The Lightweight Approach to Use Grid Services with Grid Widgets on Grid WebOS 在网格WebOS上使用网格服务和网格小部件的轻量级方法

2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing Pub Date : 2010-05-17 DOI: 10.1109/CCGRID.2010.25

Yi-Lun Pan, Chang-Hsing Wu, Chia-Yen Liu, Hsi-En Yu, Weicheng Huang

引用次数: 0

Multi-FFT Vectorization for the Cell Multicore Processor Cell多核处理器的多fft矢量化

2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing Pub Date : 2010-05-17 DOI: 10.1109/CCGRID.2010.78

J. Barhen, T. Humble, P. Mitra, M. Traweek

引用次数: 3

Performance Analysis of Diffusion Tensor Imaging in an Academic Production Grid 学术生产网格中扩散张量成像性能分析

2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing Pub Date : 2010-05-17 DOI: 10.1109/CCGRID.2010.21

D. Krefting, R. Lützkendorf, Kathrin Peter, J. Bernarding

{"title":"Performance Analysis of Diffusion Tensor Imaging in an Academic Production Grid","authors":"D. Krefting, R. Lützkendorf, Kathrin Peter, J. Bernarding","doi":"10.1109/CCGRID.2010.21","DOIUrl":"https://doi.org/10.1109/CCGRID.2010.21","url":null,"abstract":"Analysis of diffusion weighted magnetic resonance images serves increasingly for non-invasive tracking of nerve fibers in the human brain, both in clinical diagnosis and basic research. Diffusion-tensor imaging (DTI) enables in-vivo research on the internal structure of the central nervous system, an estimation of the interconnection of functional areas and diagnosis of brain tumors and de-myelinating diseases. But modeling the local diffusion parameters is computationally expensive and on standard desktop computers runtimes of up to days are common. A workflow based grid implementation of the algorithm with slice-based parallelization has shown significant speedup. However, in production use, the implementation frequently delayed and even failed, discouraging the medical collaborators to take up the management of the data processing themselves. Therefore a comprehensive analysis of possible sources for errors and delays as well as their real impact in the respective infrastructure is vital to enable clinical researchers to fully exploit the benefits of the Healthgrid application. In this manuscript, we tested different implementations of the DTI analysis with respect to robustness and runtime. Based on the results, concrete application improvements as well as general suggestions for the layout and maintenance of Healthgrids are concluded.","PeriodicalId":444485,"journal":{"name":"2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122733833","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7

A Capabilities-Aware Programming Model for Asymmetric High-End Systems 非对称高端系统的能力感知编程模型

2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing Pub Date : 2010-05-17 DOI: 10.1109/CCGRID.2010.131

M. M. Rafique

引用次数: 0