Detecção de Overbooking em Aplicações Baseadas em Docker Através de Aprendizagem de Máquina
Resumo
O artigo propõe um modelo de aprendizado de máquina para detectar ambientes Kubernetes com overbook de recursos em um contêiner do Docker. As métricas do aplicativo e do sistema foram coletadas continuamente, as quais foram usadas como entrada para o modelo para identificar interferência causada por multi-tenancy. Os experimentos foram executados em um cluster Kubernetes, com um aplicativo de Big Data baseado em contêiner, o que mostrou que o modelo pode detectar overbooking de recursos com precisões de até 98% em um cenário que pode causar degradação no desempenho do aplicativo, com taxas de overbooking de até 1,2.
Referências
Abreu, V., Santin, A. O., Viegas, E. K., and Cogo, V. V. (2020). Identity and access management for IoT in smart grid. In Advanced Information Networking and Applications, pages 1215–1226. Springer International Publishing.
Bulle, B. B., Santin, A. O., Viegas, E. K., and dos Santos, R. R. (2020). A host-based intrusion detection model based on OS diversity for SCADA. In IECON 2020 The 46th Annual Conference of the IEEE Industrial Electronics Society. IEEE.
Caglar, F. and Gokhale, A. (2014). ioverbook: Intelligent resource-overbooking to support soft real-time applications in the cloud. In 2014 IEEE 7th International Conference on Cloud Computing, pages 538–545.
Duc, T. L., Leiva, R. G., Casari, P., and Östberg, P.-O. (2019). Machine learning methods for reliable resource provisioning in edge-cloud computing: A survey. ACM Comput. Surv., 52(5).
Hoeflin, D. and Reeser, P. (2012). Quantifying the performance impact of overbooking virtualized resources. In 2012 IEEE International Conference on Communications (ICC), pages 5523–5527.
Horchulhack, P., Viegas, E. K., and Santin, A. O. (2022). Toward feasible machine learning model updates in network-based intrusion detection. Computer Networks, 202:108618.
Tomás, L. and Tordsson, J. (2014). Cloud service differentiation in overbooked data centers. In 2014 IEEE/ACM 7th International Conference on Utility and Cloud Computing, pages 541–546.
Truyen, E., Van Landuyt, D., Reniers, V., Rafique, A., Lagaisse, B., and Joosen, W. (2016). Towards a container-based architecture for multi-tenant saas applications. In International Workshop on Adaptive and Reflective Middleware, ARM 2016.
Venkateswaran, S. and Sarkar, S. (2019). Time-sensitive provisioning of bare metal compute as a cloud service. In 2019 IEEE 12th International Conference on Cloud Computing (CLOUD), pages 447–451.
Vicentini, C., Santin, A., Viegas, E., and Abreu, V. (2018). A machine learning auditing model for detection of multi-tenancy issues within tenant domain. In ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID), pages 543–552.
Viegas, E., Santin, A. O., and Jr, V. A. (2021). Machine learning intrusion detection in big data era: A multi-objective approach for longer model lifespans. IEEE Transactions on Network Science and Engineering, 8(1):366–376.
Zhong, Z. and Buyya, R. (2020). A cost-efficient container orchestration strategy in kubernetes-based cloud computing infrastructures with heterogeneous resources. ACM Trans. Internet Technol., 20(2).
Bulle, B. B., Santin, A. O., Viegas, E. K., and dos Santos, R. R. (2020). A host-based intrusion detection model based on OS diversity for SCADA. In IECON 2020 The 46th Annual Conference of the IEEE Industrial Electronics Society. IEEE.
Caglar, F. and Gokhale, A. (2014). ioverbook: Intelligent resource-overbooking to support soft real-time applications in the cloud. In 2014 IEEE 7th International Conference on Cloud Computing, pages 538–545.
Duc, T. L., Leiva, R. G., Casari, P., and Östberg, P.-O. (2019). Machine learning methods for reliable resource provisioning in edge-cloud computing: A survey. ACM Comput. Surv., 52(5).
Hoeflin, D. and Reeser, P. (2012). Quantifying the performance impact of overbooking virtualized resources. In 2012 IEEE International Conference on Communications (ICC), pages 5523–5527.
Horchulhack, P., Viegas, E. K., and Santin, A. O. (2022). Toward feasible machine learning model updates in network-based intrusion detection. Computer Networks, 202:108618.
Tomás, L. and Tordsson, J. (2014). Cloud service differentiation in overbooked data centers. In 2014 IEEE/ACM 7th International Conference on Utility and Cloud Computing, pages 541–546.
Truyen, E., Van Landuyt, D., Reniers, V., Rafique, A., Lagaisse, B., and Joosen, W. (2016). Towards a container-based architecture for multi-tenant saas applications. In International Workshop on Adaptive and Reflective Middleware, ARM 2016.
Venkateswaran, S. and Sarkar, S. (2019). Time-sensitive provisioning of bare metal compute as a cloud service. In 2019 IEEE 12th International Conference on Cloud Computing (CLOUD), pages 447–451.
Vicentini, C., Santin, A., Viegas, E., and Abreu, V. (2018). A machine learning auditing model for detection of multi-tenancy issues within tenant domain. In ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID), pages 543–552.
Viegas, E., Santin, A. O., and Jr, V. A. (2021). Machine learning intrusion detection in big data era: A multi-objective approach for longer model lifespans. IEEE Transactions on Network Science and Engineering, 8(1):366–376.
Zhong, Z. and Buyya, R. (2020). A cost-efficient container orchestration strategy in kubernetes-based cloud computing infrastructures with heterogeneous resources. ACM Trans. Internet Technol., 20(2).
Publicado
23/05/2022
Como Citar
HORCHULHACK, Pedro; VIEGAS, Eduardo; SANTIN, Altair; RAMOS, Felipe.
Detecção de Overbooking em Aplicações Baseadas em Docker Através de Aprendizagem de Máquina. In: WORKSHOP DE TRABALHOS DE INICIAÇÃO CIENTÍFICA E DE GRADUAÇÃO - SIMPÓSIO BRASILEIRO DE REDES DE COMPUTADORES E SISTEMAS DISTRIBUÍDOS (SBRC), 40. , 2022, Fortaleza/CE.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2022
.
p. 209-216.
ISSN 2177-9384.
DOI: https://doi.org/10.5753/sbrc_estendido.2022.223437.
