A new approach for dispatching task flows in GRID systems with inalienable resources
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Abstract
This paper presents a new approach for solving the problem of dispatching task flows with known complexity in GRID systems that have inalienable resources with determined performance. The proposed method is simple to implement and is compared with the commonly used FCFS method. An example of a practical problem that can be solved using this method is provided.
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This work is licensed under a Creative Commons Attribution 4.0 International License.
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Accepted 2022-11-20
Published 2022-11-21
References
Carastan-Santos, D., De Camargo, R.Y. and Trystram, D., 2019. One can only gain by replacing EASY Backfilling, a simple scheduling policies case study. 19th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID). IEEE, pp.1–10. Available from: https://doi.org/10.1109/CCGRID.2019.00010. DOI: https://doi.org/10.1109/CCGRID.2019.00010
Choi, S., Kim, H., Byun, E. and Hwang, C., 2006. A Taxonomy of Desktop Grid Systems Focusing on Scheduling. KU-CSE-2006-1120-02.
Dheenadayalan, K., Muralidhara, V.N. and Srinivasaraghavan, G., 2016. Storage Load Control Through Meta-Scheduler Using Predictive Analytics. In: N. Bjørner, S. Prasad and L. Parida, eds. Distributed Computing and Internet Technology. Cham: Springer International Publishing, pp.75–86. DOI: https://doi.org/10.1007/978-3-319-28034-9_9
Google Docs, 2021. PageSpeed Insights. Available from: https://developers.google.com/speed/docs/insights/v5/about.
Haruna, A.A., Jung, L.T. and Zakaria, N., 2015. Design and Development of Hybrid Integrated Thermal Aware Job Scheduling on Computational Grid Environment. 2015 International Symposium on Mathematical Sciences and Computing Research. pp.13–17. Available from: https://doi.org/10.1109/ismsc.2015.7594020. DOI: https://doi.org/10.1109/ISMSC.2015.7594020
Kaur, M., 2017. Multi-objective Evolution-Based Scheduling of Computational Intensive Applications in Grid Environment. Proceedings of the International Conference on Data Engineering and Communication Technology. pp.457–467. Available from: https://doi.org/10.1007/978-981-10-1678-3_44. DOI: https://doi.org/10.1007/978-981-10-1678-3_44
Kokilavani, T. and Amalarethinam, D.I.G., 2011. Load Balanced Min-Min Algorithm for Static Meta-Task Scheduling in Grid Computing. International Journal of Computer Applications, 20(2), pp.43–49. DOI: https://doi.org/10.5120/2403-3197
Kropyvnytska, V.B., Klim, B.V., Romanchuk, A.G. and Slabinoga, M.O., 2011. Investigation of scheduling algorithms in computer systems. Rozvidka ta rozrobka naftovykh i hazovykh rodovyshch, 2(39), pp.93–105.
Kumar, P.S., Parthiban, L. and Jegatheeswari, V., 2019. Privacy and security issues in cloud computing using idyllic approach Latha Parthiban. Networking and Virtual Organisations, 21(1), pp.30–42. Available from: https://doi.org/10.1504/IJNVO.2019.101146. DOI: https://doi.org/10.1504/IJNVO.2019.101146
Microsoft Docs, 2021. What Is Windows Communication Foundation — WCF. Available from: https://docs.microsoft.com/en-us/dotnet/framework/wcf/whats-wcf.
Naithani, P., 2018. Genetic Algorithm Based Scheduling To Reduce Energy Consumption In Cloud. 2018 Fifth International Conference on Parallel, Distributed and Grid Computing. IEEE, pp.616–620. Available from: https://doi.org/10.1109/pdgc.2018.8745801. DOI: https://doi.org/10.1109/PDGC.2018.8745801
Node, S., 2021. Desktop grids, connecting everyone to science. Available from: https://sciencenode.org/feature/desktop-grids-connecting-everyonescience.php.
Pujiyanta, A. and Nugroho, L.E., 2018. Planning and Scheduling Jobs on Grid Computing. 2018 International Symposium on Advanced Intelligent Informatics. IEEE, pp.162–166. Available from: https://doi.org/10.1109/icic47613.2019.8985978. DOI: https://doi.org/10.1109/SAIN.2018.8673372
Ramyachitra, D. and Kumar, P.P., 2016. Frog leap algorithm for homology modelling in grid environment. Journal of Emerging Technologies and Innovative Research, 7(1). DOI: https://doi.org/10.1504/IJGUC.2016.073775
Sahana, S., 2019. Evolutionary based hybrid GA for solving multi-objective grid scheduling problem. Microsystem Technologies. Available from: https://doi.org/10.1007/s00542-019-04673-z. DOI: https://doi.org/10.1007/s00542-019-04673-z
Thet, Y., Hlaing, H. and Yee, T.T., 2019. Static Independent Task Scheduling on Virtualized Servers in Cloud Computing Environment. 2019 International Conference on Advanced Information Technologies. IEEE, pp.55–59. Available from: https://doi.org/10.1109/aitc.2019.8920865. DOI: https://doi.org/10.1109/AITC.2019.8920865
Uzdenov, T., 2021. A New Task Scheduling Algorithm for GRID Systems with Nonalienable Resources. In: A. Zaporozhets and V. Artemchuk, eds. Systems, Decision and Control in Energy II. Cham: Springer International Publishing, pp.207–220. Available from: https://doi.org/10.1007/978-3-030-69189-9_12. DOI: https://doi.org/10.1007/978-3-030-69189-9_12
Uzdenov, T., 2021. Simulator of Task Sheduling in Geographicaly Distributed Computer systemswith Non-Alienable Resources. Electronic Modeling, 42(1). DOI: https://doi.org/10.15407/emodel.43.01.117