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Job Scheduling Strategies for Parallel Processing 10th International Workshop, JSSPP 2004, New York, NY, USA, June 13, 2004, Revised Selected Papers /

This volume contains the papers presented at the 10th Anniversary Workshop on Job Scheduling Strategies for Parallel Processing. The workshop was held in New York City, on June 13, 2004, at Columbia University, in conjunction with the SIGMETRICS 2004 conference. Although it is a workshop, the papers...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor Corporativo: SpringerLink (Online service)
Otros Autores: Feitelson, Dror (Editor ), Rudolph, Larry (Editor ), Schwiegelshohn, Uwe (Editor )
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2005.
Edición:1st ed. 2005.
Colección:Theoretical Computer Science and General Issues, 3277
Temas:
Acceso en línea:Texto Completo
Tabla de Contenidos:
  • Parallel Job Scheduling - A Status Report
  • Scheduling on the Top 50 Machines
  • Parallel Computer Workload Modeling with Markov Chains
  • Enhancements to the Decision Process of the Self-Tuning dynP Scheduler
  • Reconfigurable Gang Scheduling Algorithm
  • Time-Critical Scheduling on a Well Utilised HPC System at ECMWF Using Loadleveler with Resource Reservation
  • Inferring the Topology and Traffic Load of Parallel Programs Running in a Virtual Machine Environment
  • Multi-toroidal Interconnects: Using Additional Communication Links to Improve Utilization of Parallel Computers
  • Costs and Benefits of Load Sharing in the Computational Grid
  • Workload Characteristics of a Multi-cluster Supercomputer
  • A Dynamic Co-allocation Service in Multicluster Systems
  • Exploiting Replication and Data Reuse to Efficiently Schedule Data-Intensive Applications on Grids
  • Performance Implications of Failures in Large-Scale Cluster Scheduling
  • Are User Runtime Estimates Inherently Inaccurate?
  • Improving Speedup and Response Times by Replicating Parallel Programs on a SNOW
  • LOMARC - Lookahead Matchmaking for Multi-resource Coscheduling.