An Optimal Time-Based Resource Allocation for Biomedical Workflow Applications in Cloud

References

• J. Yu, R. Buyya, and K. Ramamohanarao, “Workflow scheduling algorithms for grid computing in metaheuristics for scheduling in distributed computing environments,” in Studies in computational intelligence. Vol. 146, F. Xhafa, A. Abraham, Eds. Berlin: Springer, 2008, pp. 173–214.
   Google Scholar
• R. Singh, and S. Singh, “Score based deadline constrained workflow scheduling algorithm for cloud systems,” Int. J. Cloud Comput. Serv. Archit., Vol. 3, no. 6, pp. 31–41, Dec. 2013.
   Google Scholar
• I. Gupta, M. S. Kumar, and P. K. Jana, “Compute-intensive workflow scheduling in multi-cloud environment,” in Proceedings of International Conference on Advances in Computing, Communications and Informatics (ICACCI), Jaipur, Sept. 2016, pp. 315–21.
   Google Scholar
• W. Chen, and E. Deelman, “Workflowsim: a toolkit for simulating scientific workflows in distributed Environments,” in Proceedings of 8th IEEE International Conference on eScience (eScience 2012), Chicago, Jan. 2013, pp. 1–8.
   Google Scholar
• P. Heinzlreiter, J. R. Perkins, Ó. Torreño, J. Karlsson, J. A. Ranea, A. Mitterecker, M. Blanca, and O. Trelles, “A cloud-based GWAS analysis pipeline for clinical researchers,” in Proceedings of 4th International Conference on Cloud Computing and Services Science (CLOSER 2014), SCITEPRESS - Barcelona: Science and Technology Publications., Apr. 2014, pp. 387–94.
   Google Scholar
• Y. Zhao, X. Fei, I. Raicu, and S. Lu, “Opportunities and challenges in running scientific workflows on the cloud,” in Proceedings of International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, Beijing, Oct. 2011, pp. 455–62.
   Google Scholar
• F. Llwaah, N. Thomas, and J. Ca la, “Improving MCT scheduling algorithm to reduce the makespan and cost of workflow execution in the cloud,” in 31st UK Performance Engineering Workshop, 17 Sept. 2015, pp. 1–8.
   Google Scholar
• S. Prathibha, B. Latha, and G. Suamthi, “Particle swarm optimization based workflow scheduling for medical applications in cloud,” J. Biomed. Res. Health Sci. Bio Convergence Technol., Vol. 28, pp. 380–5, Nov. 2017.
   PubMedGoogle Scholar
• A. Verma, and S. Kaushal, “Bi-criteria priority based particle swarm optimization workflow scheduling algorithm for cloud,” in Proceedings of International Conference on Recent Advances in Engineering and Computational Sciences (RAECS), Chandigarh, Mar. 2014, pp. 1–6.
   Google Scholar
• I. Gupta, M. S. Kumar, and P. K. Jana, “Transfer time-aware workflow scheduling for multi-cloud environment,” in Proceedings of International Conference on Computing, Communication and Automation (ICCCA), Noida, Apr. 2016, pp. 732–7.
   Google Scholar
• A. Bala, and I. Chana, “Multilevel priority-based task scheduling algorithm for workflows in cloud computing environment,” in Proceedings of International Conference on ICT for Sustainable Development Advances in Intelligent Systems and Computing, Ahmedabad, Feb. 2015, Vol. 408, pp. 685–93.
   Google Scholar
• J. Thaman, and M. Singh, “Green cloud environment by using robust planning algorithm,” J.Egypt. Inf., Vol. 18, no. 3, pp. 1–10, Feb. 2017.
   Google Scholar
• N. Mohanapriya, G. Kousalya, and P. Balakrishnan, “Cloud workflow scheduling algorithms: a survey,” Int. J. Adv. Eng. Technol., Vol. 7, no. 3, pp. 188–95, Sep. 2016.
   Google Scholar
• I. F. Senturk, P. Balakrishnan, A. Abu-Doleha, K. Kayaa, Q. Malluhi, and U. V. Catalyurek, “A resource provisioning framework for bioinformatics applications in multi-cloud environments,” J. Future Gener. Comput. Syst., Vol. 78, pp. 379–391, Jan 2018. doi: 10.1016/j.future.2016.06.008
   Web of Science ®Google Scholar
• A.M. Middleton, “Data-intensive technologies for cloud computing,” in Handbook of Cloud Computing, B. Furht, A. Escalante, Eds. Boston, MA: Springer, 2010, pp. 83–136.
   Google Scholar
• H. Topcuouglu, S. Hariri, and M. y. wu, “Performance-effective and low-complexity task scheduling for heterogeneous computing,” IEEE Trans. Parallel Distrib. Syst, Vol. 13, no. 3, pp. 260–74, Mar. 2002. doi: 10.1109/71.993206
   Web of Science ®Google Scholar
• T. Fahringer, A. Jugravu, S. Pllana, R. Prodan, C. Seragiotto, and H. L. Truong, “ASKALON: a tool set for cluster and grid computing,” Concurr Comput. Pract. Exp., Vol. 17, no. 2, pp. 143–69, Feb. 2005. doi: 10.1002/cpe.929
   Web of Science ®Google Scholar
• P. Blaha, K. Schwarz, G. K. H. Madsen, D. Kvasnicka, and J. Luitz. “Wien2k” An Augmented Plane Wave Plus Local Orbitals Program for Calculating Crystal Properties. 2nd ed. Vienna: Vienna University of Technology, 2001.
   Google Scholar
• P. Rutschmann, and D. Theiner, “An inverse modelling approach for the estimation of hydrological model parameters,” J. Hydro Inf. (2005).
   Google Scholar