Energy Aware Workload Scheduling Metrics for Execution of Parallel Application in Heterogeneous Cloud Computing Platform
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-62269-4_40
Reference16 articles.
1. Ahmad, Z., et al.: Scientific workflows management and scheduling in cloud computing: taxonomy, prospects, and challenges. IEEE Access 9, 53491–53508 (2021). https://doi.org/10.1109/ACCESS.2021.3070785
2. Qin, Y., Wang, H., Yi, S., Li, X., Zhai, L.: An energy-aware scheduling algorithm for budget-constrained scientific workflows based on multi-objective reinforcement learning. J. Supercomput. 76(1), 455–480 (2020)
3. Majewski, M., Pawlik, M., Malawski, M.: Algorithms for scheduling scientific workflows on serverless architecture. In: 2021 IEEE/ACM 21st International Symposium on Cluster, Cloud and Internet Computing (CCGrid), pp. 782–789 (2021). https://doi.org/10.1109/CCGrid51090.2021.00095
4. Li, X., Yu, W., Ruiz, R., Zhu, J.: Energy-aware cloud workflow applications scheduling with geo-distributed data. IEEE Trans. Serv. Comput. https://doi.org/10.1109/TSC.2020.2965106
5. Tang, X., Fu, Z.: CPU–GPU utilization aware energy-efficient scheduling algorithm on heterogeneous computing systems. IEEE Access 8, 58948–58958 (2020). https://doi.org/10.1109/ACCESS.2020.2982956
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