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Job Scheduling Strategies for Parallel Processing: 24th International Workshop, JSSPP 2021, Virtual Event, May 21, 2021, Revised Selected Papers de Dalibor Klusáček

Job Scheduling Strategies for Parallel Processing: 24th International Workshop, JSSPP 2021, Virtual Event, May 21, 2021, Revised Selected Papers: Lecture Notes in Computer Science, cartea 12985

Editat de Dalibor Klusáček, Walfredo Cirne, Gonzalo P. Rodrigo
en Limba Engleză Paperback – 6 oct 2021
This book constitutes the thoroughly refereed post-conference proceedings of the 24th International Workshop on Job Scheduling Strategies for Parallel Processing, JSSPP 2021, held as a virtual event in May 2021 (due to the Covid-19 pandemic). The 10 revised full papers presented were carefully reviewed and selected from 17 submissions. In addition to this, one keynote paper was included in the workshop. The volume contains two sections: Open Scheduling Problems and Proposals and Technical Papers. The papers cover such topics as parallel computing, distributed systems, workload modeling, performance optimization, and others.
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Specificații

ISBN-13: 9783030882235
ISBN-10: 3030882233
Pagini: 231
Ilustrații: XII, 231 p. 103 illus., 93 illus. in color.
Dimensiuni: 155 x 235 x 16 mm
Greutate: 0.35 kg
Ediția:1st ed. 2021
Editura: Springer International Publishing
Colecția Springer
Seriile Lecture Notes in Computer Science, Theoretical Computer Science and General Issues

Locul publicării:Cham, Switzerland

Cuprins

Keynote.- ​Resampling with Feedback: A New Paradigm of Using Workload Data for Performance Evaluation.- Open Scheduling Problems and Proposals.- Collection of Job Scheduling Prediction Methods.- Modular Workload Format: extending SWF for modular systems.- Technical Papers.- Measurement and Modeling of Performance of HPC Applications towards Overcommitting Scheduling Systems.- Scheduling Microservice Containers on Large Core Machines through Placement and Coalescing.- Learning-based Approaches to Estimate Job Wait Time in HTC Datacenters.- A HPC Co-Scheduler with Reinforcement Learning.- Performance-Cost Optimization of Moldable Scientific Workflows.- Temperature-Aware Energy-Optimal Scheduling of Moldable Streaming Tasks onto 2D-Mesh-Based Many-Core CPUs with DVFS.- Scheduling Challenges for Variable Capacity Resources.- GLUME: A Strategy for Reducing Workflow Execution Times on Batch-Scheduled Platforms.