Job Scheduling Strategies for Parallel Processing
24th International Workshop, JSSPP 2021, Virtual Event, May 21, 2021, Revised Selected Papers
Published on 5. October 2021
XII, 231 pages
978-3-030-88224-2 (ISBN)
Description
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.
More details
Series
Edition
1st ed. 2021
Language
English
Place of publication
Cham
Switzerland
Publishing group
Springer International Publishing
Product notice
Reflowable
Illustrations
XII, 231 p. 103 illus., 93 illus. in color.
File size
17,42 MB
ISBN-13
978-3-030-88224-2 (9783030882242)
DOI
10.1007/978-3-030-88224-2
Schweitzer Classification
Other editions
Additional editions
Dalibor Klusácek | Walfredo Cirne | Gonzalo P. Rodrigo
Job Scheduling Strategies for Parallel Processing
24th International Workshop, JSSPP 2021, Virtual Event, May 21, 2021, Revised Selected Papers
Book
10/2021
Springer
€69.54
Shipment within 7-9 days
Persons
Content
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.
