An experimental study of scheduling algorithms for many-task applications

The paper studies the performance of algorithms for scheduling of many-task applications in distributed computing systems. Two important classes of such applications are considered: bags-of-tasks and workflows. The comparison of algorithms is performed on the basis of discrete-event simulation for v...

Full description

Saved in:
Bibliographic Details
Published in:The Journal of supercomputing 2019-12, Vol.75 (12), p.7857-7871
Main Authors: Sukhoroslov, Oleg, Nazarenko, Alexey, Aleksandrov, Roman
Format: Article
Language:English
Subjects:
Algorithms
Compilers
Computer networks
Computer Science
Computer simulation
Dependence
Discrete event systems
Distributed processing
Experiments
Interpreters
Mathematical models
Model accuracy
Processor Architectures
Programming Languages
Scheduling
Simulation
Task scheduling
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The paper studies the performance of algorithms for scheduling of many-task applications in distributed computing systems. Two important classes of such applications are considered: bags-of-tasks and workflows. The comparison of algorithms is performed on the basis of discrete-event simulation for various application cases and system configurations. The developed simulation framework based on SimGrid toolkit provides the necessary tools for implementation of scheduling algorithms, generation of synthetic systems and applications, execution of simulation experiments and analysis of results. This allowed to perform a large number of experiments in a reasonable amount of time and to ensure reproducible results. The conducted experiments demonstrate the dependence of the performance of studied algorithms on various application and system characteristics. While confirming the performance advantage of advanced static algorithms, the presented results reveal some interesting insights. In particular, the accuracy of the used network model helped to demonstrate the limitations of simple analytical models for scheduling of data-intensive parallel applications with static algorithms.
ISSN:0920-8542
1573-0484
DOI:10.1007/s11227-018-2553-9