Optimizing Resource Allocation in Hierarchically Distributed Data Centers
Resumo
The current networks infrastructure needs to support the rapidly increasing data traffic. Sophisticated planning approaches must be adopted by the operators so the high number of applications can be managed efficiently. In this work, a resource provisioning model for hierarchically distributed data centers is proposed using Integer Linear Programming (ILP). The objective is to increase the efficiency in the use of computational resources and decrease the overhead in network links. Results show that, the model is able to efficiently accommodate 20% more applications when compared to the First-Fit approach.
Palavras-chave:
Redes de Centros de Dados, Alocação de Recursos, Problemas de Otimização
Referências
Chen, W., Wang, D., and Li, K. (2018). Multi-user multi-task computation offloading in green mobile edge cloud computing. IEEE Transactions on Services Computing.
Computing, F. (2015). the internet of things: Extend the cloud to where the things are. Available on: http://www.cisco. com/c/dam/en_us/solutions/trends/iot/docs/computingoverview.pdf.
da Silva, C. N., Wosinska, L., Spadaro, S., Costa, J. C., Francês, C. R., and Monti, P. (2016). Restoration in optical cloud networks with relocation and services differentiation. Journal of Optical Communications and Networking, 8(2):100–111.
Dolui, K. and Datta, S. K. (2017). Comparison of edge computing implementations: Fog computing, cloudlet and mobile edge computing. In Global Internet of Things Summit (GIoTS), 2017, pages 1–6. IEEE.
Liao, C., Shou, G., Liu, Y., Hu, Y., and Guo, Z. (2017). Intelligent traffic accident detection system based on mobile edge computing. In Computer and Communications (ICCC), 2017 3rd IEEE International Conference on, pages 2110–2115. IEEE.
Masoudi, M. and Cavdar, C. (2017). Cloud vs edge computing for mobile services: Delay-aware decision making to minimize energy consumption. arXiv preprint arXiv:1711.03771.
Mei, J., Li, K., and Li, K. (2017). Customer-satisfaction-aware optimal multiserver configuration for profit maximization in cloud computing. T-SUSC, 2(1):17–29.
Sturzinger, E., Tornatore, M., and Mukherjee, B. (2017). Application-aware resource provisioning in a heterogeneous internet of things. In Optical Network Design and Modeling (ONDM), 2017 International Conference on, pages 1–6. IEEE.
Upadhyaya, J. and Ahuja, N. J. (2017). Quality of service in cloud computing in higher education: A critical survey and innovative model. In I-SMAC (IoT in Social, Mobile, Analytics and Cloud)(I-SMAC), 2017 International Conference on, pages 137–140. IEEE.
Zhang, J., Xia, W., Yan, F., and Shen, L. (2018). Joint computation offloading and resource allocation optimization in heterogeneous networks with mobile edge computing. IEEE Access, 6:19324–19337.
Zhang, X., Mao, Y., Zhang, J., and Letaief, K. B. (2017). Multi-objective resource allocation for mobile edge computing systems. In Personal, Indoor, and Mobile Radio Communications (PIMRC), 2017 IEEE 28th Annual International Symposium on, pages 1–5. IEEE.
Computing, F. (2015). the internet of things: Extend the cloud to where the things are. Available on: http://www.cisco. com/c/dam/en_us/solutions/trends/iot/docs/computingoverview.pdf.
da Silva, C. N., Wosinska, L., Spadaro, S., Costa, J. C., Francês, C. R., and Monti, P. (2016). Restoration in optical cloud networks with relocation and services differentiation. Journal of Optical Communications and Networking, 8(2):100–111.
Dolui, K. and Datta, S. K. (2017). Comparison of edge computing implementations: Fog computing, cloudlet and mobile edge computing. In Global Internet of Things Summit (GIoTS), 2017, pages 1–6. IEEE.
Liao, C., Shou, G., Liu, Y., Hu, Y., and Guo, Z. (2017). Intelligent traffic accident detection system based on mobile edge computing. In Computer and Communications (ICCC), 2017 3rd IEEE International Conference on, pages 2110–2115. IEEE.
Masoudi, M. and Cavdar, C. (2017). Cloud vs edge computing for mobile services: Delay-aware decision making to minimize energy consumption. arXiv preprint arXiv:1711.03771.
Mei, J., Li, K., and Li, K. (2017). Customer-satisfaction-aware optimal multiserver configuration for profit maximization in cloud computing. T-SUSC, 2(1):17–29.
Sturzinger, E., Tornatore, M., and Mukherjee, B. (2017). Application-aware resource provisioning in a heterogeneous internet of things. In Optical Network Design and Modeling (ONDM), 2017 International Conference on, pages 1–6. IEEE.
Upadhyaya, J. and Ahuja, N. J. (2017). Quality of service in cloud computing in higher education: A critical survey and innovative model. In I-SMAC (IoT in Social, Mobile, Analytics and Cloud)(I-SMAC), 2017 International Conference on, pages 137–140. IEEE.
Zhang, J., Xia, W., Yan, F., and Shen, L. (2018). Joint computation offloading and resource allocation optimization in heterogeneous networks with mobile edge computing. IEEE Access, 6:19324–19337.
Zhang, X., Mao, Y., Zhang, J., and Letaief, K. B. (2017). Multi-objective resource allocation for mobile edge computing systems. In Personal, Indoor, and Mobile Radio Communications (PIMRC), 2017 IEEE 28th Annual International Symposium on, pages 1–5. IEEE.
Publicado
06/05/2019
Como Citar
VIEIRA, Rafael F.; TEIXEIRA, Carlos A. M.; CARDOSO, Diego L..
Optimizing Resource Allocation in Hierarchically Distributed Data Centers. In: SIMPÓSIO BRASILEIRO DE REDES DE COMPUTADORES E SISTEMAS DISTRIBUÍDOS (SBRC), 37. , 2019, Gramado.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2019
.
p. 556-565.
ISSN 2177-9384.
DOI: https://doi.org/10.5753/sbrc.2019.7386.
