Publication Details
Performance-Cost Optimization of Moldable Scientific Workflows
task graph scheduling, workflow, genetic algorithm, moldable tasks, makespan
estimation
Moldable scientific workflows represent a special class of scientific workflows
where the tasks are written as distributed programs being able to exploit various
amounts of computer resources. However, current cluster job schedulers require
the user to specify the amount of resources per task manually. This often leads
to suboptimal execution time and related cost of the whole workflow execution
since many users have only limited experience and knowledge of the parallel
efficiency and scaling. This paper proposes several mechanisms to automatically
optimize the execution parameters of moldable workflows using genetic algorithms.
The paper introduces a local optimization of workflow tasks, a global
optimization of the workflow on systems with on-demand resource allocation, and
a global optimization for systems with static resource allocation. Several
objectives including the workflow makespan, computational cost and the percentage
of idling nodes are investigated together with a trade-off parameter putting
stress on one objective or another. The paper also discusses the structure and
quality of several evolved workflow schedules and the possible reduction in
makespan or cost. Finally, the computational requirements of evolutionary process
together with the recommended genetic algorithm settings are investigated. The
most complex workflows may be evolved in less than two minutes using the global
optimization while in only 14s using the local optimization.
@inproceedings{BUT175770,
author="Marta {Jaroš} and Jiří {Jaroš}",
title="Performance-Cost Optimization of Moldable Scientific Workflows",
booktitle="Job Scheduling Strategies for Parallel Processing",
year="2021",
series="Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
journal="Lecture Notes in Computer Science",
number="12985",
pages="149--167",
publisher="Springer International Publishing",
address="Portland, Oregon USA",
doi="10.1007/978-3-030-88224-2\{_}8",
isbn="978-3-030-88223-5",
issn="0302-9743",
url="https://link.springer.com/book/10.1007%2F978-3-030-88224-2"
}