Machine Learning-Based Online Scheduling in Distributed Computing
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Publisher
Springer International Publishing
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-30445-3_21
Reference20 articles.
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2. Rodriguez, M.A., Buyya, R.: Scheduling dynamic workloads in multi-tenant scientific workflow as a service platforms. Future Gener. Comput. Syst. 79(P2), 739–750 (2018)
3. Kurowski, K., Nabrzyski, J., Oleksiak, A., Weglarz, J.: multicriteria aspects of grid resource management. In: Nabrzyski, J., Schopf, J.M., Weglarz J. (eds.) Grid Resource Management. State of the Art and Future Trends, pp. 271–293. Kluwer Academic Publishers. (2003)
4. Toporkov, V., Yemelyanov, D.: Coordinated and hindsight resources allocation in distributed computing. In: Proceedings - 2019 20th International Conference on Parallel and Distributed Computing, Applications and Technologies, PDCAT (2019)
5. Toporkov, V., Yemelyanov, D., Toporkova, A.: Coordinated global and private job-flow scheduling in grid virtual organizations. J. Simul. Model. Pract. Theor. 107, 102228 (2021)
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