K8sGAScheduler: Algoritmo para alocação inteligente de recursos em cluster kubernetes
Resumo
A implementação do Kubernetes na administração de aplicações em ambientes de nuvem de borda proporciona escalabilidade, confiabilidade e automação. No entanto, o gerenciamento de clusters enfrenta desafios como a heterogeneidade de recursos e a dinamicidade do ambiente. Embora o Kubernetes forneça recursos para lidar com esses desafios, a otimização da alocação de pods é uma questão complexa que requer soluções avançadas. Este artigo introduz o K8sGAScheduler, uma técnica de agendamento baseada em Algoritmo Genético desenvolvida para otimizar a alocação de pods em clusters Kubernetes. Essa abordagem leva em conta o consumo de recursos, a comunicação entre os pods e as restrições de capacidade dos nós, visando encontrar uma alocação que maximize a eficiência do cluster. Os resultados obtidos, por meio de simulação, demonstram melhorias em relação ao scheduler padrão do Kubernetes, oferecendo insights para o gerenciamento mais eficiente de recursos em clusters Kubernetes e delineando para futuras pesquisas nessa área.Referências
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Townend, P., Clement, S., Burdett, D., Yang, R., Shaw, J., Slater, B., and Xu, J. (2019). Invited paper: Improving data center efficiency through holistic scheduling in kubernetes. In 2019 IEEE International Conference on Service-Oriented System Engineering (SOSE), pages 156–15610.
Wojciechowski, , Opasiak, K., Latusek, J., Wereski, M., Morales, V., Kim, T., and Hong, M. (2021). Netmarks: Network metrics-aware kubernetes scheduler powered by service mesh. In IEEE INFOCOM 2021 - IEEE Conference on Computer Communications, pages 1–9.
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Zhang, X., Li, L., Wang, Y., Chen, E., and Shou, L. (2021). Zeus: Improving resource efficiency via workload colocation for massive kubernetes clusters. IEEE Access, 9:105192–105204.
Botez, R., Costa-Requena, J., Ivanciu, I.-A., Strautiu, V., and Dobrota, V. (2021). Sdn-based network slicing mechanism for a scalable 4g/5g core network: A kubernetes approach. Sensors, 21(11).
Burns, B., Beda, J., Hightower, K., and Evenson, L. (2022). Kubernetes: up and running: dive into the future of infrastructure. O’Reilly Media, Beijing Boston, third edition edition.
Chirivella Perez, E., Alcaraz Calero, J., Wang, Q., and Gutiérrez-Aguado, J. (2018). Orchestration architecture for automatic deployment of 5g services from bare metal in mobile edge computing infrastructure. Wireless Communications and Mobile Computing, 2018.
Chu, P. and Beasley, J. (1998). Journal of Heuristics, 4(1):63–86.
dell’Amico, M., Delorme, M., Iori, M., and Martello, S. (2019). Mathematical models and decomposition methods for the multiple knapsack problem. Eur. J. Oper. Res., 274:886–899.
Farhi, E., Goldstone, J., Gutmann, S., Lapan, J., Lundgren, A., and Preda, D. (2001). A quantum adiabatic evolution algorithm applied to random instances of an np-complete problem. Science, 292(5516):472–475.
Martello, S. and Toth, P. (1980). Solution of the zero-one multiple knapsack problem. European Journal of Operational Research, 4(4):276–283. Combinational Optimization.
Menouer, T. (2021). Kcss: Kubernetes container scheduling strategy. The Journal of Supercomputing.
Mfula, H., Ylä-Jääski, A., and Nurminen, J. (2021). Seamless kubernetes cluster management in multi-cloud and edge 5g applications. In International Conference on High Performance Computing Simulation (HPCS 2020). International Conference on High Performance Computing amp; Simulation, HPCS ; Conference date: 22-03-2021 Through 27-03-2021.
Nguyen, H. T., Do, T. V., and Rotter, C. (2021). Scaling upf instances in 5g/6g core with deep reinforcement learning. IEEE Access, PP:1–1.
Rejiba, Z. and Chamanara, J. (2022). Custom scheduling in kubernetes: A survey on common problems and solution approaches. ACM Comput. Surv., 55(7).
Roeva, O., Fidanova, S., and Paprzycki, M. (2013). Influence of the population size on the genetic algorithm performance in case of cultivation process modelling. In 2013 Federated Conference on Computer Science and Information Systems, pages 371–376.
Santos, J., Wauters, T., Volckaert, B., and De Turck, F. (2019). Towards network-aware resource provisioning in kubernetes for fog computing applications. In 2019 IEEE Conference on Network Softwarization (NetSoft), pages 351–359.
Townend, P., Clement, S., Burdett, D., Yang, R., Shaw, J., Slater, B., and Xu, J. (2019). Invited paper: Improving data center efficiency through holistic scheduling in kubernetes. In 2019 IEEE International Conference on Service-Oriented System Engineering (SOSE), pages 156–15610.
Wojciechowski, , Opasiak, K., Latusek, J., Wereski, M., Morales, V., Kim, T., and Hong, M. (2021). Netmarks: Network metrics-aware kubernetes scheduler powered by service mesh. In IEEE INFOCOM 2021 - IEEE Conference on Computer Communications, pages 1–9.
Xiong, J., Chen, B., He, Z., Guan, W., and Chen, Y. (2021). Optimal design of community shuttles with an adaptive-operator-selection-based genetic algorithm. Transportation Research Part C: Emerging Technologies, 126:103109.
Zhang, X., Li, L., Wang, Y., Chen, E., and Shou, L. (2021). Zeus: Improving resource efficiency via workload colocation for massive kubernetes clusters. IEEE Access, 9:105192–105204.
Publicado
24/05/2024
Como Citar
TAVARES, Thiago; SANTOS, Carlos; CARDOSO, Kleber; OLIVEIRA-JR, Antonio.
K8sGAScheduler: Algoritmo para alocação inteligente de recursos em cluster kubernetes. In: WORKSHOP DE GERÊNCIA E OPERAÇÃO DE REDES E SERVIÇOS (WGRS), 29. , 2024, Niterói/RJ.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 182-195.
ISSN 2595-2722.
DOI: https://doi.org/10.5753/wgrs.2024.3285.
