Job Scheduling Strategies for Parallel Processing
7th International Workshop, JSSPP 2001, Cambridge, MA, USA, June 16, 2001, Revised Papers
Published on 30. October 2001
Book
Paperback/Softback
VIII, 216 pages
978-3-540-42817-6 (ISBN)
Description
This book constitutes the thoroughly refereed post-proceedings of the 7th International Workshop on Job Scheduling Strategies for Parallel Processing, JSSPP 2001, held in Cambridge, MA, USA, in June 2001.
The 11 revised full papers presented were carefully selected and improved during two rounds of reviewing and revision, and present state-of-the-art results in the area.
The 11 revised full papers presented were carefully selected and improved during two rounds of reviewing and revision, and present state-of-the-art results in the area.
More details
Series
Language
English
Place of publication
Berlin
Germany
Publishing group
Springer Berlin
Target group
Professional and scholarly
Research
Illustrations
VIII, 216 p.
Dimensions
Height: 235 mm
Width: 155 mm
Thickness: 13 mm
Weight
347 gr
ISBN-13
978-3-540-42817-6 (9783540428176)
DOI
10.1007/3-540-45540-X
Schweitzer Classification
Other editions
Additional editions
Dror G. Feitelson | Larry Rudolph
Job Scheduling Strategies for Parallel Processing
7th International Workshop, JSSPP 2001, Cambridge, MA, USA, June 16, 2001, Revised Papers
E-Book
06/2003
Springer
€53.49
Available for download
Content
Performance Evaluation with Heavy Tailed Distributions.- SRPT Scheduling for Web Servers.- An Efficient and Scalable Coscheduling Technique for Large Symmetric Multiprocessor Clusters.- Coscheduling under Memory Constraints in a NOW Environment.- The Influence of Communication on the Performance of Co-allocation.- Core Algorithms of the Maui Scheduler.- On the Development of an Efficient Coscheduling System.- Effects of Memory Performance on Parallel Job Scheduling.- An Integrated Approach to Parallel Scheduling Using Gang-Scheduling, Backfilling, and Migration.- Characteristics of a Large Shared Memory Production Workload.- Metrics for Parallel Job Scheduling and Their Convergence.