Job Scheduling Strategies for Parallel Processing
Description
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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Content
.- 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.