A Resource Broker for Computing Nodes Selection in Grid Computing Environments

Yang, Chao‐Tung; Lai, Chuan-Lin; Shih, Po-Chi; Li, Kuan‐Ching

doi:10.1007/978-3-540-30208-7_141

Cited by 17 publications

(14 citation statements)

References 2 publications

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“…In this study, Getter and Setter provides a new function that responds to information-collection and data-access requests submitted by users. (4) Predictor: It periodically retrieves raw data from the Message Center (modular design allows various types of raw data for adaptation to our previous prediction models [10,19]), and accepts requests issued by the Agent (described below) to predict and obtain required data. This component was applicable to any applications that need to predict future values.…”

Section: Machine and Network Information Aggregatormentioning

confidence: 99%

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

Yang

Leu

Chen

2010

Concurrency and Computation

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SUMMARYGrid 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.

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Section: Machine and Network Information Aggregatormentioning

confidence: 99%

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

Yang

Leu

Chen

2010

Concurrency and Computation

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“…We perform this step by two actions. First, transfer the machine list needed for MPI [17,[19][20][21][22] to the first machine in our machine list via GridFTP, because the first is the best choice based on the application model. The second action is to transfer the application to every available resource.…”

Section: Phase 5-job Executionmentioning

confidence: 99%

“…Figure 2 shows the resource broker system architecture and component relationships; the functions of each component are listed in the relation link. A common grid portal allows easy access to the system [17,21]. A schematic diagram of the complete workflow system is shown in Fig.…”

Section: Conceptual Overviewmentioning

confidence: 99%

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

2008

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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.

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“…Grids enable organizations to make optimal use of information technology resources. And grids offer a means to act as a utility bureau in providing information technology to commercial clients who pay only for what they use, as with electricity or water [1,[3][4][5][6][7][8][9][10][11][12][14][15][16][17][18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

A resource broker with an efficient network information model on grid environments

et al. 2007

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This paper describes a resource broker whose main function is to match available resources to user needs. The resource broker provides a uniform interface for accessing available and appropriate resources via user credentials. We also focus on providing approximate measurement models for network-related information using NWS for future scheduling and benchmarking. We first propose a network measurement model for gathering network-related information (including bandwidth, latency, forecasting, error rates, etc.) without generating excessive system overhead. Second, we constructed a grid platform using Globus Toolkit that integrates the resources of five schools in Taichung integrated grid environment resources (TIGER). The resource broker runs on top of TIGER. Therefore, it provides security and current information about available resources and serves as a link to the diverse systems available in the Grid.

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A Resource Broker for Computing Nodes Selection in Grid Computing Environments

Cited by 17 publications

References 2 publications

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

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

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

A resource broker with an efficient network information model on grid environments

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