Fuzzy-based Security-Driven Optimistic Scheduling of Scientific Workflows in Cloud Computing

REFERENCES

• J. Angela Jennifa Sujana, T. Revathi, T. S. Siva Priya, and K. Muneeswaran, “Soft computing,” 2017. DOI: 10.1007/s00500-017-2897-8
   Google Scholar
• H. Topcuoglu, S. Hariri, and M. Wu, “Performance-effective and Low-complexity task scheduling for heterogeneous computing,” IEEE Trans. Parallel Distrib. Sys., Vol. 13, no. 3, pp. 260–74, Mar. 2002. doi: 10.1109/71.993206
   Web of Science ®Google Scholar
• H. El-Rewini and T. G. Lewis, “Scheduling parallel program tasks onto arbitrary target machines,” J. Parallel Distrib. Comput., Vol. 9, no. 2, pp. 138–53, 1990. doi: 10.1016/0743-7315(90)90042-N
   Web of Science ®Google Scholar
• G. C. Sih and E. A. Lee, “A compile-time scheduling heuristic for interconnection-constrained heterogeneous processor architectures,” IEEE Trans. Parallel Distrib. Syst., Vol. 4, no. 2, pp. 175–87, Feb. 1993. doi: 10.1109/71.207593
   Web of Science ®Google Scholar
• M. A. Iverson, F. O. Özgüner, and G. J. Follen, “Parallelizing existing applications in a distributed heterogeneous environment,” in Proceedings of the Fourth Heterogeneous Computing Workshop (HCW ‘95), pp. 93–100, 1995.
   Google Scholar
• H. Oh and S. Ha, “A static scheduling heuristic for heterogeneous processors,” in Proceedings of the Euro-Par ‘96 Parallel Processing, Lyon, France, pp. 573–77, 1996.
   Google Scholar
• H. Topcuoglu, S. Hariri, and M. Wu, “Task scheduling algorithms for heterogeneous processors,” in Proceedings of the Eighth Heterogeneous Computing Workshop (HCW), San Juan, Puerto Rico, USA, 1999, pp. 3–14.
   Google Scholar
• J. Angela Jennifa Sujana, T. Revathi, and M. Malarvizhili, “Scheduling of scientific workflows in cloud with replication,” Applied Mathematical Sciences, Vol. 9, no. 46, pp. 2273–80, 2015.
   Google Scholar
• T. Hagras and J. Janecek, “A simple scheduling heuristic for heterogeneous computing environments,” in IEEE Computer Society, in Parallel and Distributed Computing, International Symposium, Ljubljana, Slovenia, Oct. 2003, pp. 104–4.
   Google Scholar
• E. Ilavarasan and P. Thambidurai, “Low complexity performance effective task scheduling algorithm for heterogeneous computing environments,” J. Comput. Sci., Vol. 3, no. 2, pp. 94–103, 2007. doi: 10.3844/jcssp.2007.94.103
   Google Scholar
• E. Ilavarasan, P. Thambidurai, and R. Mahilmannan, “High performance task scheduling algorithm for heterogeneous computing system,” Distrib. Parallel Comput. Springer LNCS, Vol. 3719, pp. 193–203, 2005. doi: 10.1007/11564621_22
   Web of Science ®Google Scholar
• L. F. Bittencourt, R. Sakellariou, and E. R. M. Madeira, “DAG scheduling using a lookahead variant of the heterogeneous earliest finish time algorithm,” in Proceedings of the 18th Euromicro International Conference Parallel, Distributed and Network-Based Processing (PDP ‘10), Pisa, Italy, 2010, pp. 27–34.
   Google Scholar
• X. Tang, K. Li, G. Liao, and R. Li, “List scheduling with duplication for heterogeneous computing systems,” J. Parallel Distrib. Comput., Vol. 70, pp. 323–9, 2010. doi: 10.1016/j.jpdc.2010.01.003
   Web of Science ®Google Scholar
• H. Arabnejad and J. G. Barbosa, “List scheduling algorithm for heterogeneous system by an optimistic cost table,” IEEE Trans. Parallel Distrib. Sys., Vol. 25, no. 3, pp. 682–694, Mar. 2014. doi: 10.1109/TPDS.2013.57
   Web of Science ®Google Scholar
• X. Tang, K. Li, Z. Zeng, and B. Veeravalli, “A novel security-driven scheduling algorithm for precedence-constrained tasks In heterogeneous distributed systems,” IEEE Trans. Comput., Vol. 60, no. 7, pp. 1017–29, Jul. 2011.
   Web of Science ®Google Scholar
• W. Wang, G. Zeng, D. Tang, and J. Yao, “Cloud-DLS: Dynamic trusted scheduling for cloud computing,” Expert Syst. Appl., Vol. 39, pp. 2321–9, 2012. doi: 10.1016/j.eswa.2011.08.048
   Web of Science ®Google Scholar
• T. Maa, Y. Chub, L. Zhaoc, and O. Ankhbayarb, “Resource allocation and scheduling in cloud computing: policy and algorithm,” IETE Tech. Rev., Vol. 31, no. 1, pp. 4–16, 2014. doi: 10.1080/02564602.2014.890837
   Web of Science ®Google Scholar
• A. Rajendran, P. N. Rao, and N. K. Tewari, “A method of static scheduling for simulation and implementation of FMS,” IETE J. Res., Vol. 35, no. 4, pp. 237–45, 1989. doi: 10.1080/03772063.1989.11436819
   Google Scholar
• M. Sajid and Z. Raza, “Turnaround time minimization-based static scheduling model using task duplication for fine-grained parallel applications onto hybrid cloud environment,” IETE J. Res., Vol. 62, no. 3, pp. 402–14, 2016. doi: 10.1080/03772063.2015.1075911
   Web of Science ®Google Scholar
• S. Song, K. Hwang, and Y.-K. Kwok, “Risk-resilient heuristics and genetic algorithms for security-assured grid job scheduling,” IEEE Trans. Comput., Vol. 55, no. 6, pp. 703–19, Jun. 2006. doi: 10.1109/TC.2006.89
   Web of Science ®Google Scholar
• Q. Y. TinghuaiMaa, W. Liua, D. Guanb, and S. Leeb,” Grid task scheduling: Algorithm review,” IETE Tech. Rev., Vol. 28, no. 2, pp. 158–67, 2011. doi: 10.4103/0256-4602.76138
   Web of Science ®Google Scholar
• T. A. L. Genez, L. F. Bittencourt, R. Sakellariou, and E. R. M. Madeira, “A flexible scheduler for workflow ensembles,” in UCC ‘16: Proceedings of the 9th International Conference on Utility and Cloud Computing, Nov. 2016, pp. 55–62.
   Google Scholar
• A. F. Barsoum and M. A. Hasan, “Enabling dynamic data and indirect mutual trust for cloud computing storage systems,” IEEE Trans. Parallel Distrib. Syst., Vol. 24, no. 12, pp. 2375–85, Dec. 2013. doi: 10.1109/TPDS.2012.337
   Web of Science ®Google Scholar
• K. Li, X. Tang, B. Veeravalli, and K. Li, “Scheduling precedence constrained stochastic tasks on heterogeneous cluster systems,” IEEE Trans. Comput., Vol. 64, no. 1, pp. 191–204, Jan. 2015. doi: 10.1109/TC.2013.205
   Web of Science ®Google Scholar
• S. Sharma and P. Kuila, “Design of dependable task scheduling algorithm in cloud environment,” WCI ‘15: Proceedings of the Third International Symposium on Women in Computing and Informatics, Aug. 2015, pp. 516–21.
   Google Scholar
• H. Arabnejad, J. G. Barbosa, and R. Prodan, “Low-time complexity budget-deadline constrained workflow scheduling on heterogeneous resources,” Future Gener. Comp. Syst., Vol. 55, pp. 29–40, 2016. doi: 10.1016/j.future.2015.07.021
   Web of Science ®Google Scholar
• J. J. Durillo, R. Prodan, and J. G. Barbosa, “Pareto tradeoff scheduling of workflows on federated commercial clouds,” Simul. Modell. Pract. Theory, 58, pp. 95–111, 2015. doi: 10.1016/j.simpat.2015.07.001
   Web of Science ®Google Scholar
• J. J. Durillo and R. Prodan, “Multi-objective workflow scheduling in Amazon EC2,” Cluster Comput., Vol. 17, no. 2, pp. 169–89, 2014. doi: 10.1007/s10586-013-0325-0
   Web of Science ®Google Scholar
• R. Duan, R. Prodan, and X. Li, “A sequential cooperative game theoretic approach to scheduling multiple large-scale applications in grids,” Future Gener. Comp. Syst., 30, pp. 27–43, 2014. doi: 10.1016/j.future.2013.09.001
   Web of Science ®Google Scholar
• T. Xie and X. Qin, “Security-aware resource allocation for real-time parallel jobs on homogeneous and heterogeneous clusters,” IEEE Trans. Parallel Distrib. Syst., Vol. 19, no. 5, pp. 682–97, May 2008. doi: 10.1109/TPDS.2007.70776
   Web of Science ®Google Scholar
• T. Xie and X. Qin, “Scheduling security-critical real-time applications on clusters,” IEEE Trans. Comput., Vol. 55, no. 7, pp. 864–79, Jul. 2006. doi: 10.1109/TC.2006.110
   Web of Science ®Google Scholar
• I. Bilogrevic, M. Jadliwala, P. Kumar, S. S. Walia, J.-P. Hubaux, I. Aad, and V. Niemi, “Meetings through the cloud: Privacy-preserving scheduling on mobile devices,” J. Syst. Software, Vol. 84, pp. 1910–27, 2011. doi: 10.1016/j.jss.2011.04.027
   Web of Science ®Google Scholar
• L. C. Canon, E. Jeannot, R. Sakellariou, and W. Zheng, “Comparative evaluation of the robustness of Dag scheduling heuristics,” in Grid Computing: Achievements and Prospects, S. Gorlatch, P. Fragopoulou, and T. Priol, Eds., Boston: Springer, 2008, pp. 73–84.
   Google Scholar
• W. Tan, Y. Sun, L. X. Li, G. Z. Lu, and T. Wang, “A trust service-oriented scheduling model for workflow applications in cloud computing,” IEEE Syst. J., Vol. 8, no. 3, pp. 868–78, Sep. 2014. doi: 10.1109/JSYST.2013.2260072
   Web of Science ®Google Scholar
• L. J. M. Azevedo, J. C. Estrella, C. F. M. Toledo, and S. Reiff-Marganiec, “An analysis of metaheuristic to sla establishment in cloud computing,” Central European Workshop on Services and their Composition, 2017, Lugano – Switzerland. 9th Central European Workshop on Services and their Composition, 2017, Vol. 1, pp. 79–84.
   Google Scholar
• M. Salehand and L. Dong, “Real-time scheduling with security enhancement for packet switched networks,” IEEE Trans. Network Serv. Manage., Vol. 10, no. 3, pp. 271–85, Sep. 2013. doi: 10.1109/TNSM.2013.071813.120299
   Web of Science ®Google Scholar
• W. Chen and E. Deelman, “ Workflowsim: A toolkit for simulating scientific workflows in distributed environments,” in E-Science (e-Science), IEEE 8th International Conference, pp. 1–8, 2012.
   Google Scholar
• R. N. Calheiros, R. Ranjan, A. Beloglazov, C. A. De Rose, and R. Buyya, “Cloudsim: A toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms,” Software Pract. Experience, Vol. 41, no.1, pp. 23–50, 2011. doi: 10.1002/spe.995
   Web of Science ®Google Scholar
• G. Juve, A. Chervenak, E. Deelman, S. Bharathi, G. Mehta, and K. Vahi, “Characterizing and profiling scientific workflows,” J. Future Gener. Comput. Syst., Vol. 29, no. 3, pp. 682–92, 2012. doi: 10.1016/j.future.2012.08.015
   Web of Science ®Google Scholar
• Montage: An Astronomical Image Mosaic Engine. Available: http://montage.ipac.caltech.edu/. Accessed July 29, 2015.
   Google Scholar
• “USC Epigenome Center,” Available: http://epigenome.usc.edu.
   Google Scholar
• Pegasus Workflow Management System. Available: https://pegasus.isi.edu/projects/pegasus/. Accessed July 29, 2015.
   Google Scholar