Job Scheduling Strategies for Parallel Processing (eBook, PDF)

28th International Workshop, JSSPP 2025, Milan, Italy, June 3-4, 2025, Revised Selected Papers
Redaktion: Klusácek, Dalibor; Rodrigo, Gonzalo P.; Corbalán, Julita

• Format: PDF

Produktbeschreibung

This book constitutes the refereed proceedings of the 28th International Workshop on Job Scheduling Strategies for Parallel Processing, JSSPP 2025, held in Milan, Italy, during June 3-4, 2025.

The 17 full papers and 1 keynote paper presented in this book were carefully reviewed and selected from 25 submissions. These papers covered interesting topics within the resource management and scheduling domains.

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Produktdetails

• Verlag: Springer Nature Switzerland
• Seitenzahl: 350
• Erscheinungstermin: 1. Januar 2026
• Englisch
• ISBN-13: 9783032105073
• Artikelnr.: 76269108

Inhaltsangabe

.
How to make the ultimate goal of energy
efficient data centers a reality. .
Power
Aware Scheduling for Multi
Center HPC Electricity Cost Optimization. .
Job Grouping Based Intelligent Resource Prediction Framework. .
Kubernetes Scheduling with Checkpoint/Restore: Challenges and Open Problems. .
Adaptive Carbon
Aware scheduling policies for HPC systems. .
Resource elasticity for scientific platforms on HPC infrastructure. .
More for Less: Integrating Capability
Predominant and Capacity
Predominant Computing. .
Workflow Batch Job Scheduling with Considering Task Dependencies. .
Quality
Aware Energy
Efficient Scheduling of Moldable
Parallel Streaming Computations on Heterogeneous Multicore CPUs with DVFS. .
Optimizing Energy Efficiency in Heterogeneous Computing via Multi
Objective Scheduling with Reinforcement Learning. .
Static powercap vs. EAR hard
powercap: Performance evaluation. .
Deep RC: A Scalable Data Engineering and Deep Learning Pipeline. .
Fedsort: An Optimized Federated Scheduling Strategy for Cloud Workloads with Inter
task Dependencies. .
Evaluating the Impact of Algorithmic Components on Task Graph Scheduling. .
Communication
balanced Job Allocation using SLURM. .
Performance Models to support HPC Co
Scheduling. .
ELiSE: A tool to support algorithmic design for HPC co
scheduling. .
Deadline Miss Minimization Scheduling for License
Constrained CAE Jobs in Hybrid Cloud Infrastructure.