Abstract
In this work, an approach for a preference-based job-flow scheduling in Grid virtual organizations (VOs) is proposed and studied. Users’ and resource providers’ preferences, VOs internal policies, resources geographical distribution along with local private utilization impose specific requirements for efficient scheduling according to different, usually contradictive, criteria. Fair scheduling policies in VOs assume resources distribution according to VO stakeholders individual preferences. The main idea is to perform additional optimization during the resources selection step which may be used in a variety of scheduling procedures, such as Backfilling or First Fit. We consider a target optimization criterion as a linear combination of global (group) and private (user) job scheduling criteria. The mutual importance factor between the private and the global criteria is introduced to achieve a balanced scheduling solution.
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Acknowledgements
This work was partially supported by the Council on Grants of the President of the Russian Federation for State Support of Young Scientists (grant YPhD-2979.2019.9), RFBR (grants 18-07-00456 and 18-07-00534), and by the Ministry on Education and Science of the Russian Federation (project no. 2.9606.2017/8.9).
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Toporkov, V., Toporkova, A., Yemelyanov, D. (2020). Global and Private Job-Flow Scheduling Optimization in Grid Virtual Organizations. In: Kotenko, I., Badica, C., Desnitsky, V., El Baz, D., Ivanovic, M. (eds) Intelligent Distributed Computing XIII. IDC 2019. Studies in Computational Intelligence, vol 868. Springer, Cham. https://doi.org/10.1007/978-3-030-32258-8_18
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