Job Scheduling Strategies for Parallel Processing, Kartoniert / Broschiert

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.- 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.