![]() In: Heterogeneous Computing Workshop, 1999 (HCW'99) Proceedings. ![]() Topcuoglu, H., Hariri, S., Wu, M.-Y.: Task scheduling algorithms for heterogeneous processors. Kwok, Y.-K.K.Y.-K., Ahmad, I.: A static scheduling algorithm using dynamic critical path for assigning parallel algorithms onto multiprocessors. In: Proceedings of the 2017 IEEE 10th International Conference on Cloud Computing (CLOUD) 2017, pp. 99–112ĭimopoulos, S., Krintz, C., Wolski, R.: Pythia: Admission control for multi-framework, deadline-driven, big data workloads. In: Proceedings of the 7th ACM European Conference on Computer Systems 2012, pp. IEEEįerguson, A.D., Bodik, P., Kandula, S., Boutin, E., Fonseca, R.: Jockey: Guaranteed job latency in data parallel clusters. In: 2010 IEEE Second International Conference on Cloud Computing Technology and Science 2010, pp. Kc, K., Anyanwu, K.: Scheduling hadoop jobs to meet deadlines. Zhu, X., Wang, J., Guo, H., Zhu, D., Yang, L.T., Liu, L.: Fault-tolerant scheduling for real-time scientific workflows with elastic resource provisioning in virtualized clouds. Concurrency and Computation: Practice and Experience. ![]() Rodriguez, M.A., Buyya, R.: A taxonomy and survey on scheduling algorithms for scientific workflows in IaaS cloud computing environments. Nedić, N., Vukmirović, S., Imre, L., Čapko, D.: A genetic algorithm approach for utility management system workflow scheduling. Singh, L., Singh, S.: A survey of workflow scheduling algorithms and research issues. Yu, J., Buyya, R.: A taxonomy of scientific workflow systems for grid computing. Li, X., Cai, Z.: Elastic resource provisioning for cloud workflow applications. ![]() In: Proceedings of the International Conference on Cloud Computing 2009, pp. Ostermann, S., Iosup, A., Yigitbasi, N., Prodan, R., Fahringer, T., Epema, D.: A performance analysis of EC2 cloud computing services for scientific computing. In: Proceedings of the Mastering cloud computing: foundations and applications programming. ![]() Experiments show that DQWS outperforms its competitors, both in terms of meeting deadlines and minimizing the monetary costs of executing scheduled workflows.īuyya, R., Vecchiola, C., Selvi, S.T.: High-throughput computing. Scheduling linear graphs is performed in the final phase of the algorithm. The process continues until only chain structured workflows, called linear graphs, remain. DQWS finds the critical path, schedules it, removes the critical path from the workflow, and effectively divides the leftover into some mini workflows. The critical path concept is the inspiration behind the divide-and-conquer process. The proposed Divide-and-conquer Workflow Scheduling algorithm ( DQWS) is designed with the objective of minimizing the cost of workflow execution while respecting its deadline. The present paper introduces a new direction based on a divide-and-conquer approach to scheduling these workflows. It is a challenging assignment to schedule such workflows in the cloud while also considering users’ different quality of service requirements. The modeling of complex computational applications as giant computational workflows has been a critically effective means of better understanding the intricacies of applications and of determining the best approach to their realization. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |