Information Technology Reference
In-Depth Information
As future work, we plan to extend our platform to support a disaster deci-
sion support system (DDSS). The principles presented in this chapter will
be further expanded so the DDSS will provide a dashboard for the strategic,
tactical, and operational decisions arising during disaster mitigation. It will
be integrated with a range of modeling and simulation tools to provide opti-
mization models with up-to-date situational awareness and predictions to
provide recommendations to authorities. This extension will support not
only workflow applications but also other programming models suitable
for clouds, such as MapReduce. Ideally, the platform will support not only
applications that are entirely described as one of these models but also com-
plex applications that are composed of diverse subcomponents that may be
developed as different programming models.
References
1. Deelman, E., Singh, G., Su, M., et al. 2005. Pegasus: a framework for mapping com-
plex scientific workflows onto distributed systems. Scientific Computing 13:219-237.
2. Oinn, T., Greenwood, M., Addis, M., et al. 2006. Taverna: lessons in creating
a workflow environment for the life sciences. Concurrency and Computation:
Practice and Experience 18:1067-1100.
3. Taylor, I., Shields, M., Wang, I., et al. 2007. The Triana Workflow Environment:
Architecture and Applications. In Workflows for E-Science , ed. I. J. Taylor,
E. Deelman, D. B. Gannon, et al., 320-339. London: Springer.
4. Pandey, S., Karunamoorthy, D., and Buyya, R. 2011. Workflow engine for
clouds. In Cloud Computing: Principles and Paradigms , ed. R. Buyya, J. Broberg,
and A. Goscinski, 321-344. New York: Wiley.
5. Kwok, Y., and Ahmad, I. 1999. Static scheduling algorithms for allocating
directed task graphs to multiprocessors. ACM Computing Surveys 3:406-471.
6. Yu, J., Buyya, R., and Ramamohanarao, K. 2008. Workflow scheduling algorithms
for grid computing. In Metaheuristics for Scheduling in Distributed Computing
Environments , ed. F. Xhafa and A. Abraham, 173-214. Berlin: Springer.
7. Hirales-Carbajal, A., Tchernykh, A., Yahyapour, R., et al. 2012. Multiple work-
flow scheduling strategies with user run time estimates on a grid. Journal of Grid
Computing 10:325-346.
8. Li, X., Calheiros, R., Lu, S., et al. 2012. Design and development of an adap-
tive workflow-enabled spatial-temporal analytics framework. In Proceedings of
the 2012 IEEE International Workshop on Scalable Computing for Big Data Analytics
(SC-BDA 2012) , 862-867. Piscataway, NJ: IEEE Computer Society.
9. Bryan, L. 2010. The social and psychological issues of high-density city space.
In Designing High-Density Cities for Social and Environmental Sustainability , ed .
E. Ng, 285-292. London: Earthscan.
10. Singapore Department of Statistics. 2013. Singapore in figures 2013. http://www.
singstat.gov.sg/Publications/publications_and_papers/reference/sif2013.pdf.
Search WWH ::




Custom Search