Information Technology Reference
In-Depth Information
with time constraints. For this, we plan to implement a Cloud scheduler based on SI
techniques in order to
fairly
schedule users' VMs
/
ff
jobs based on di
erent optimization
criteria (e.g., cost, execution times, etc.).
Finally, another interesting issue consists of providing more elaborated dynamic op-
timization capabilities, enabling the dynamic reallocation (migration) of VMs from one
physical machine to another to meet a specific optimization criteria such as improving
the response time, reducing the number of physical resources in use for minimizing
energy consumption, or balancing the workload of all resources to avoid resources sat-
uration and performance slowdown. In addition, the user could also specify constraints
for the scheduler decisions such as hardware (amount of CPU, memory, bandwidth,
etc.), platform (type of hypervisor, operating system, etc.), location (geographical re-
strictions), among others.
Acknowledgments.
We acknowledge the financial support provided by ANPCyT
through grants PICT-2012-0045 and PICT-2012-2731, and National University of Cuyo
project 06B
308. The first author acknowledges her Ph.D. fellowship granted by the Na-
tional Scientific and Technological Research Council (CONICET).
/
References
1. Agostinho, L., Feliciano, G., Olivi, L., Cardozo, E., Guimaraes, E.: A Bio-inspired approach
to provisioning of virtual resources in federated Clouds. In: Ninth International Conference
on Dependable, Autonomic and Secure Computing (DASC), DASC 2011, December 12-14,
pp. 598-604. IEEE (2011)
2. Calheiros, R., Ranjan, R., Beloglazov, A., De Rose, C., Buyya, R.: Cloudsim: A toolkit for
modeling and simulation of Cloud Computing environments and evaluation of resource pro-
visioning algorithms. Software: Practice & Experience 41(1), 23-50 (2011)
3. Celesti, A., Fazio, M., Villari, M., Puliafito, A.: Virtual machine provisioning through satel-
lite communications in federated Cloud environments. Future Generation Computer Sys-
tems 28(1), 85-93 (2012)
4. de Oliveira, G., Ribeiro, E., Ferreira, D., Araújo, A., Holanda, M., Walter, M.: ACOsched: a
scheduling algorithm in a federated Cloud infrastructure for bioinformatics applications. In:
International Conference on Bioinformatics and Biomedicine, pp. 8-14. IEEE (2013)
5. Gahlawat, M., Sharma, P.: Survey of virtual machine placement in federated Clouds. In:
International Advance Computing Conference (IACC), pp. 735-738. IEEE (2014)
6. García Garino, C., Ribero Vairo, M., Andía Fagés, S., Mirasso, A., Ponthot, J.P.: Numeri-
cal simulation of finite strain viscoplastic problems. Journal of Computational and Applied
Mathematics 246, 174-184 (2013)
7. García Garino, C., Gabaldón, F., Goicolea, J.M.: Finite element simulation of the simple
tension test in metals. Finite Elements in Analysis and Design 42(13), 1187-1197 (2006)
8. Jung, J., Jung, S., Kim, T., Chung, T.: A study on the Cloud simulation with a network topol-
ogy generator. World Academy of Science, Engineering & Technology 6, 303-306 (2012)
9. Kennedy, J.: Swarm Intelligence. In: Zomaya, A. (ed.) Handbook of Nature-Inspired and
Innovative Computing, pp. 187-219. Springer, US (2006)
10. Lucas-Simarro, J., Moreno-Vozmediano, R., Montero, R., Llorente, I.: Scheduling strategies
for optimal service deployment across multiple clouds. Future Generation Computer Sys-
tems 29(6), 1431-1441 (2013)
