The grid2003 production grid: principles and practice

Foster, Ian; Gieraltowski, J.; Gose, S.; Maltsev, Natalia; May, E. N.; Rodriguez, Alexis; Sulakhe, Dinanath; Vaniachine, A.; Green, M.; Miller, Russ; Letts, J.; Martin, T. Fonseca; Shank, J. T.; Youssef, S.; Bury, D.; Dumitrescu, Cătălin; Engh, D.; Gardner, R. W.; Adams, T.; Baker, Richard S.; Deng, Wei; Smith, J.; Yu, Dongmei; Mambelli, M.; Smirnov, Y.; Voeckler, Jens; Wilde, Michael; Zhao, Yong; Zhao, Xin; Legrand, I.; Singh, Sarbjeet; Steenberg, Conrad; Xia, Yang; Avery, P.; Cavanaugh, R.; Kim, B.; Prescott, C.; Rodriguez, Jorge Luis; Zahn, A.; Afaq, A.; Berman, E.; Annis, J.; Bauerdick, L. A. T.; Ernst, M.; Fisk, I.; Giacchetti, L.; Graham, G.; Heavey, A.; Kaiser, J. W.; Kee, S. P. Mc; Kuropatkin, N.; Pordes, R.; Sekhri, V.; Weigand, J.; Wu, Y.; Jordan, Chris; Prewett, J.; Thomas, T. L.; Baker, Keith; Sorrillo, Lawrence; Severini, H.; Huth, J.; Clifford, Ben; Deelman, Ewa; Flon, L.; Kesselman, Carl; Mehta, Gaurang; Olomu, N.; Vahi, Karan; Allen, Matthew S.; Grundhoefer, L.; Hicks, John; Luehring, F.; Peck, Sarah A.; Quick, Rob; Simms, Stephen; De, K.; McGuigan, Patrick J; Sosebee, M.; Fekete, Gyorgy; Vandenberg, Jan; Bradley, D.; Couvares, P.; Smet, Arthur De; Kireyev, C.; Paulson, Erik K.; Roy, Alain; Cho, K.; Kwon, K.; Son, D. C.; Park, H.; Koranda, S.; Moe, B.; Canon, Shane; Jackson, Keith; Konerding, David E.; Lee, Jason R.; Olson, D.; Sakrejda, I.; Tierney, Brian; Brown, Benjamin P.; Sheldon, P.

doi:10.1109/hpdc.2004.1323544

Cited by 57 publications

(58 citation statements)

References 17 publications

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“…GNARE also expedites the process of storing the results of analyses and annotations. It is based on Grid technology (specifically Globus [16], Condor [17], and the GriPhyN virtual data system [18]) and uses the computational resources of GRID2003 [19], TeraGrid [20], and the DOE science Grid [21] to perform high-throughput computations. GNARE's flexible architecture allows users to tailor the genome analysis process to their individual needs and requirements.…”

Section: Introductionmentioning

confidence: 99%

Gnare: Automated System For High-Throughput Genome Analysis With Grid Computational Backend

Sulakhe

Rodríguez

D’Souza

et al. 2005

J Clin Monit Comput

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Section: Introductionmentioning

confidence: 99%

Gnare: Automated System For High-Throughput Genome Analysis With Grid Computational Backend

Sulakhe

Rodríguez

D’Souza

et al. 2005

J Clin Monit Comput

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“…The production multi-cluster grid Grid3 [19] (now the Open Science Grid) was experiencing a job failure rate of about 30% in 2003 where 90% of failures were due to site problem (e.g., disk failure) [19,20]. One of the European ICT infrastructures servicing hundreds of scientists, Grid'5000, suffered between 2005 and 2007 from cascading and catastrophic failures involving hundreds of computers [21].…”

Section: Failures In Real-world Distributed Systemsmentioning

confidence: 99%

The Failure Trace Archive: Enabling the comparison of failure measurements and models of distributed systems

Javadi

Kondo

Iosup

et al. 2013

Journal of Parallel and Distributed Computing

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With the increasing presence, scale, and complexity of distributed systems, resource failures are becoming an important and practical topic of computer science research. While numerous failure models and failure-aware algorithms exist, their comparison has been hampered by the lack of public failure data sets and data processing tools. To facilitate the design, validation, and comparison of fault-tolerant models and algorithms, we have created the Failure Trace Archive (FTA)-an online, public repository of failure traces collected from diverse parallel and distributed systems. In this work, we first describe the design of the archive, in particular of the standard FTA data format, and the design of a toolbox that facilitates automated analysis of trace data sets. We also discuss the use of the FTA for various current and future purposes. Second, after applying the toolbox to nine failure traces collected from distributed systems used in various application domains (e.g., HPC, Internet operation, and various online applications), we present a comparative analysis of failures in various distributed systems. underline the need for public availability of trace data from different distributed systems. Last, we show how different interpretations of the meaning of failure data can result in different conclusions for failure modeling and job scheduling in distributed systems. Our results for different interpretations show evidence that there may be a need for further revisiting existing failureaware algorithms, when applied for general rather than for domain-specific distributed systems.

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“…The participating sites are the main resource providers under various conditions. We consider in this paper that all of these sites are governed by uSLAs [3][4][5][6][7].…”

Section: Problem Domain and Target Environmentmentioning

confidence: 99%

Usage SLA‐based scheduling in Grids

Dumitrescu

Raicu

Foster

2006

Concurrency and Computation

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SUMMARYManaging usage service level agreements (uSLAs) within environments that integrate participants and resources spanning multiple physical institutions is a challenging problem. Running workloads in such environments is often a similarly challenging problem owing to the scale of the environment, and to the resource partitioning based on various sharing strategies. Also, a resource may be taken down during a job execution, be improperly set up or fail job execution. Such elements have to be taken into account whenever targeting a Grid environment for problem solving. In this paper we explore uSLA-based scheduling on a real Grid, Grid3, by means of a specific workload (the BLAST workload) and a specific scheduling framework, GRUBER (an architecture and toolkit for resource uSLA specification and enforcement). The paper provides extensive experimental results and comparisons with other scheduling strategies. We also address, in great detail, the performance of different uSLA-based site selection strategies and the overall performance in scheduling workloads over Grid3 with workload sizes ranging from 10 to 10 000 jobs.

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The grid2003 production grid: principles and practice

Cited by 57 publications

References 17 publications

Gnare: Automated System For High-Throughput Genome Analysis With Grid Computational Backend

Gnare: Automated System For High-Throughput Genome Analysis With Grid Computational Backend

The Failure Trace Archive: Enabling the comparison of failure measurements and models of distributed systems

Usage SLA‐based scheduling in Grids

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