|
1. Yannuzzi, M., et al. Key ingredients in an IoT recipe: Fog Computing, Cloud computing, and more Fog Computing. in 2014 IEEE 19th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD). 2014. IEEE. 2. Perera, C., et al., Fog computing for sustainable smart cities: A survey. 2017. 50(3): p. 32. 3. Bonomi, F., et al. Fog computing and its role in the internet of things. in Proceedings of the first edition of the MCC workshop on Mobile cloud computing. 2012. ACM. 4. Matt, C.J.B. and I.S. Engineering, Fog Computing. 2018: p. 1-5. 5. Bitam, S., S. Zeadally, and A.J.E.I.S. Mellouk, Fog computing job scheduling optimization based on bees swarm. 2018. 12(4): p. 373-397. 6. Vaquero, L.M. and L.J.A.S.C.C.R. Rodero-Merino, Finding your way in the fog: Towards a comprehensive definition of fog computing. 2014. 44(5): p. 27-32. 7. Chiang, M. and T.J.I.I.o.T.J. Zhang, Fog and IoT: An overview of research opportunities. 2016. 3(6): p. 854-864. 8. Sarkar, S. and S.J.I.N. Misra, Theoretical modelling of fog computing: A green computing paradigm to support IoT applications. 2016. 5(2): p. 23-29. 9. Kui-kui, H., X. Zai-peng, and L.J.C.S. Xin, Fog Computing Task Scheduling Strategy Based on Improved Genetic Algorithm. 2018(4): p. 22. 10. Binh, H.T.T., et al. An Evolutionary Algorithm for Solving Task Scheduling Problem in Cloud-Fog Computing Environment. in Proceedings of the Ninth International Symposium on Information and Communication Technology. 2018. ACM. 11. Deng, R., et al., Optimal workload allocation in fog-cloud computing toward balanced delay and power consumption. 2016. 3(6): p. 1171-1181. 12. He, J., et al., Time synchronization in WSNs: A maximum-value-based consensus approach. 2014. 59(3): p. 660-675. 13. Li, D. and X. Sun, Nonlinear integer programming. Vol. 84. 2006: Springer Science & Business Media. 14. Kuhn, H.W.J.N.r.l.q., The Hungarian method for the assignment problem. 1955. 2(1‐2): p. 83-97. 15. Fieldsend, J.E. and S. Singh, A multi-objective algorithm based upon particle swarm optimisation, an efficient data structure and turbulence. 2002. 16. Coello, C.A.C., G.T. Pulido, and M.S.J.I.T.o.e.c. Lechuga, Handling multiple objectives with particle swarm optimization. 2004. 8(3): p. 256-279. 17. Zhou, A., et al., Multiobjective evolutionary algorithms: A survey of the state of the art. 2011. 1(1): p. 32-49. 18. Liu, J., et al., Job scheduling model for cloud computing based on multi-objective genetic algorithm. 2013. 10(1): p. 134. 19. Jena, R.J.P.C.S., Multi objective task scheduling in cloud environment using nested PSO framework. 2015. 57: p. 1219-1227. 20. Fard, H.M., et al. A multi-objective approach for workflow scheduling in heterogeneous environments. in Cluster, Cloud and Grid Computing (CCGrid), 2012 12th IEEE/ACM International Symposium on. 2012. IEEE. 21. Doğan, A. and F.J.T.C.J. Özgüner, Biobjective scheduling algorithms for execution time–reliability trade-off in heterogeneous computing systems. 2005. 48(3): p. 300-314. 22. Yin, Y., Multi-objective Task Scheduling in Cloud Environment Using Multi-objective Simplified Swarm Optimization. 2018, National Tsin Hua University. 23. Yeh, W.-C.J.I.t.o.s., man,, c.-p.A. systems, and humans, Optimization of the disassembly sequencing problem on the basis of self-adaptive simplified swarm optimization. 2011. 42(1): p. 250-261. 24. Yeh, W.-C.J.I.S., A new exact solution algorithm for a novel generalized redundancy allocation problem. 2017. 408: p. 182-197. 25. Yeh, W.-C.J.K.-B.S., Orthogonal simplified swarm optimization for the series–parallel redundancy allocation problem with a mix of components. 2014. 64: p. 1-12. 26. Yeh, W.-C.J.I.S., Novel swarm optimization for mining classification rules on thyroid gland data. 2012. 197: p. 65-76. 27. Yeh, W.-C.J.E.S.w.A., A two-stage discrete particle swarm optimization for the problem of multiple multi-level redundancy allocation in series systems. 2009. 36(5): p. 9192-9200. 28. Huang, C.-L. and W.-C.J.a.p.a. Yeh, A new SSO-based Algorithm for the Bi-Objective Time-constrained task Scheduling Problem in Cloud Computing Services. 2019. 29. Tasiopoulos, A., et al., FogSpot: Spot Pricing for Application Provisioning in Edge/Fog Computing. 2019. 30. Li, X. A non-dominated sorting particle swarm optimizer for multiobjective optimization. in Genetic and Evolutionary Computation Conference. 2003. Springer. 31. Deb, K., et al., A fast and elitist multiobjective genetic algorithm: NSGA-II. 2002. 6(2): p. 182-197. 32. Czyzżak, P. and A.J.J.o.M.C.D.A. Jaszkiewicz, Pareto simulated annealing—a metaheuristic technique for multiple‐objective combinatorial optimization. 1998. 7(1): p. 34-47. 33. Schott, J.R., Fault Tolerant Design Using Single and Multicriteria Genetic Algorithm Optimization. 1995, AIR FORCE INST OF TECH WRIGHT-PATTERSON AFB OH. 34. Van Veldhuizen, D.A., Multiobjective evolutionary algorithms: classifications, analyses, and new innovations. 1999, AIR FORCE INST OF TECH WRIGHT-PATTERSONAFB OH SCHOOL OF ENGINEERING.
|