@inproceedings{245d422186be46ce9c29e7f1ddb0a2fb,
title = "A genetic algorithm for data-aware approximate workflow scheduling",
abstract = "Data placement in complex scientific workflows gradually attracts more attention since the large amounts of data generated by these workflows significantly increases the turnaround time of the end-to-end application. It is almost impossible to make an optimal scheduling for the end-to-end workflow without considering the intermediate data movement. In order to reduce the complexity of the workflow-scheduling problem, most of the existing work constrains the problem space by some unrealistic assumptions, which result in non-optimal scheduling in practice. In this study, we propose a genetic data-aware algorithm for the end-to-end workflow scheduling problem, which performs very close to the optimal solution.",
keywords = "data, genetic algorithm, Workflows",
author = "Tevfik Kosar and Dengpan Yin",
year = "2013",
doi = "10.1109/ICECCO.2013.6718293",
language = "English",
isbn = "9781479933433",
series = "2013 International Conference on Electronics, Computer and Computation, ICECCO 2013",
publisher = "IEEE Computer Society",
pages = "322--325",
booktitle = "2013 International Conference on Electronics, Computer and Computation, ICECCO 2013",
address = "United States",
note = "2013 10th International Conference on Electronics, Computer and Computation, ICECCO 2013 ; Conference date: 07-11-2013 Through 08-11-2013",
}