References
[1]. Juve, G., Chervenak, A., Deelman, E., Bharathi, S.,
Mehta, G., & Vahi, K. (2012). Characterizing and profiling
scientific workflows. Future Generation Comput. Syst. Vol.
29(3), pp. 682- 692.
[2]. Mao, M., and Humphrey, M. (2011). Auto-scaling to
minimize cost and meet application deadlines in cloud
workflows. In Proc. Int. Conf. High Performance
Computing, Networking, Storage and Analysis (SC), pp 1-
12.
[3]. Malawski, M., Juve, G., Deelman, E., and Nabrzyski,
J. (2012). Cost-and deadline-constrained provisioning for
scientific workflow ensembles in iaas clouds. In Proc. Int.
Conf. High Performance Computing, Networking,
Storage and Anal. (SC), Vol. 22.
[4]. Abrishami, S., Naghibzadeh, M., and Epema, D.
(2012). Deadline- constrained workflow scheduling
algorithms for IaaS Clouds. Future Generation Comput.
Syst., Vol. 23(8), pp. 1400-1414.
[5]. Kennedy, J., and Eberhart, R. (1995). Particle swarm
optimization. In Proc. 6th IEEE Int. Conf. Neural Networks,
pp. 1942-1948.
[6]. Buyya, R., Broberg, J., and Goscinski, A. M. (Eds.).
(2010). Cloud computing: Principles and paradigms (Vol.
87). Wiley.
[7]. Yu, J., Buyya, R., and Tahm C. (2005). A cost based
scheduling of scientific workflow applications on utility
grids. In Proc. 1st IEEE Int. Conf. e-Sci. and Grid
Computing. (e-Science), pp. 140-147.
[8]. Pandey, S., Wu, L., Guru, S. M., & Buyya, R.
(2010). A
particle swarm optimization-based heuristic for
scheduling workflow applications in cloud computing
environments. In Proc. IEEEInt. Conf. Advanced Inform.
Networking and Applicat. (AINA), pp. 400-407.
[9]. Deb, K., Pratap, A., Agarwal, S., and Meyarivan, T.
A.
M. T. (2002). A fast and elitist multiobjective genetic
algorithm: NSGA-II. IEEE Trans. Evol. Computation. Vol.
6(2), pp. 182-197.
[10]. Mao, M., and Humphrey, M. (2012, June). A
performance study on the vm startup time in the cloud. In
Proc. 5th IEEE Int. Conf.Cloud Computing (CLOUD, pp.
423-430.
[11]. Chen, W. N., and Zhang, J. (2009). An ant colony
optimization approach to a grid workflow scheduling
problem with various QoS requirements. IEEE Trans. Syst.,
Man, Cybern., Part C:Applicat. Reviews, Vol. 39(1), pp.
29-43.
[12]. Yu, J., and Buyya, R. (2006). A budget constrained
scheduling of workflow applications on utility grids using
genetic algorithms. In Proc. 1st Workshop on Workflows in
Support of Large-Scale Sci, pp. 1-10.
[13]. Sousa, T., Silva, A., and Neves, A. (2004),
Particle
swarm based data mining algorithms for classification
tasks. Parallel Computing, Vol. 30(5), pp. 767-783.
[14]. Mell, P., and T. Grance. (2011). The NIST
definition of
cloud computing—recommendations of the National
Institute of Standards and Technology. Special Publication
800-145, NIST, Gaithersburg.
[15]. Byun, E. K., Kee, Y. S., Kim, J. S., and Maeng, S.
(2011). Costoptimized provisioning of elastic resources for
application workflows. Future Generation Comput. Syst.,
Vol. 27(8), pp. 1011-1026.
[16]. Calheiros, R. N., Ranjan, R., Beloglazov, A., De
Rose,
C. A., and Buyya, R. (2011). CloudSim: a toolkit for
modeling and simulation of cloud computing
environments and evaluation of resource provisioning
algorithms. Software: Practice and Experience, Vol. 41(1),
pp. 23-50.