Job Scheduling Strategies for Parallel Processing

.- 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.