Arquitetura de Dimensionamento Adaptativo com Suporte ao Aprendizado
Resumo
Atender às demandas de desempenho e estabilidade em aplicações sensíveis à latência é um dos principais desafios tecnológicos atuais. Este trabalho apresenta uma Arquitetura de Dimensionamento Adaptativo com Suporte ao Aprendizado, aplicada ao contexto de aplicações imersivas, baseada na combinação de métricas de hardware e eventos da aplicação para otimizar a alocação de recursos. A implementação utiliza Kubernetes (K8s) e o Kubernetes Event-driven Autoscaler (KEDA), tendo a aplicação Hubs VR como estudo de caso. Foram conduzidos experimentos que resultaram na construção de conjuntos de dados estruturados distintos: um baseado apenas em métricas de hardware e outro que integra também eventos da aplicação. Esses datasets representam um produto relevante da pesquisa, servindo como base para análises e desenvolvimento de estratégias preditivas. Os resultados indicam que a combinação de métricas pode promover respostas mais ágeis e estáveis frente às variações de carga, contribuindo para o avanço de soluções adaptativas em ambientes dinâmicos.
Palavras-chave:
Dimensionamento adaptativo, Escalonamento automático, Aplicações imersivas, MAPE-K
Referências
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Benmerar, T. Z., Theodoropoulos, T., Fevereiro, D., Rosa, L., Rodrigues, J., Taleb, T., Barone, P., Tserpes, K., and Cordeiro, L. (2023). Intelligent multi-domain edge orchestration for highly distributed immersive services: an immersive virtual touring use case. In 2023 IEEE International Conference on Edge Computing and Communications (EDGE), pages 381–392. IEEE.
Cheng, K., Zhang, S., Tu, C., Shi, X., Yin, Z., Lu, S., Liang, Y., and Gu, Q. (2023). Proscale: Proactive autoscaling for microservice with time-varying workload at the edge. IEEE Transactions on Parallel and Distributed Systems, 34(4):1294–1312.
Dimolitsas, I., Spatharakis, D., Dechouniotis, D., Zafeiropoulos, A., and Papavassiliou, S. (2023). Multi-application hierarchical autoscaling for kubernetes edge clusters. In 2023 IEEE International Conference on Smart Computing (SMARTCOMP), pages 291–296. IEEE.
Dogani, J., Namvar, R., and Khunjush, F. (2023). Auto-scaling techniques in container-based cloud and edge/fog computing: Taxonomy and survey. Computer Communications, 209:120–150.
Gonçalves, A. L. d. J., Oliveira-Jr, A., and Freitas, L. A. (2023). Revisao sistemática das aplicaçoes imersivas com base nas tecnologias habilitadoras b5g/6g, mec e ia. Anais da XI Escola Regional de Informática de Goiás.
Grafana, 2024. Grafana: The open and composable observability platform. [link]. Acesso em 21 de janeiro de 2024.
Han, B., Pathak, P., Chen, S., and Yu, L.-F. C. (2022). Comic: A collaborative mobile immersive computing infrastructure for conducting multi-user xr research. IEEE Network.
Hubs Foundation (2024). Hubs foundation - open source social vr platform. Accessed: 2024-07-12.
k6.io (2024). k6 Open Source: A modern load testing tool for developers. Acessado em 20 de dezembro de 2024.
KEDA Project (2024). Keda: Kubernetes-based event driven autoscaler. Acessado em: 10-07-2024.
Kubernetes (2022). Kubernetes documentation.
LABORA-INF-UFG (2025). Repositório do projeto adaptscale. [link].
López-Ramírez, G. A., Aragón-Zavala, A., and Vargas-Rosales, C. (2024). Exploratory data analysis for path loss measurements: Unveiling patterns and insights before machine learning. IEEE Access.
Malburg, L., Hoffmann, M., and Bergmann, R. (2023). Applying mape-k control loops for adaptive workflow management in smart factories. Journal of Intelligent Information Systems, 61(1):83–111.
Mehta, R., Sahni, J., and Khanna, K. (2023). Task scheduling for improved response time of latency sensitive applications in fog integrated cloud environment. Multimedia Tools and Applications, 82(21):32305–32328.
Nguyen, T.-T., Yeom, Y.-J., Kim, T., Park, D.-H., and Kim, S. (2020). Horizontal pod autoscaling in kubernetes for elastic container orchestration. Sensors, 20(16):4621.
Pandas (2025). Pandas: Python data analysis library. [link]. Accessed: 2025-01-13.
Pelle, I., Czentye, J., Dóka, J., and Sonkoly, B. (2019). Towards latency sensitive cloud native applications: A performance study on aws. In 2019 IEEE 12th International Conference on Cloud Computing (CLOUD), pages 272–280. IEEE.
Prometheus, T. (Acesso em 21 de janeiro de 2024). Prometheus: The Comprehensive Monitoring and Alerting Toolkit. [link].
Quattrocchi, G., Incerto, E., Pinciroli, R., Trubiani, C., and Baresi, L. (2024). Autoscaling solutions for cloud applications under dynamic workloads. IEEE Transactions on Services Computing.
Santos, J., Wauters, T., Volckaert, B., and De Turck, F. (2023). gym-hpa: Efficient auto-scaling via reinforcement learning for complex microservice-based applications in kubernetes. In NOMS 2023-2023 IEEE/IFIP Network Operations and Management Symposium, pages 1–9. IEEE.
Siriwardhana, Y., Porambage, P., Liyanage, M., and Ylianttila, M. (2021). A survey on mobile augmented reality with 5g mobile edge computing: Architectures, applications, and technical aspects. IEEE Communications Surveys & Tutorials, 23(2):1160–1192.
Taleb, T., Boudi, A., Rosa, L., Cordeiro, L., Theodoropoulos, T., Tserpes, K., Dazzi, P., Protopsaltis, A. I., and Li, R. (2022). Toward supporting xr services: Architecture and enablers. IEEE Internet of Things Journal, 10(4):3567–3586.
Wu, D., Yang, Z., Zhang, P., Wang, R., Yang, B., and Ma, X. (2023). Virtual-reality inter-promotion technology for metaverse: A survey. IEEE Internet of Things Journal.
Yuan, H. and Liao, S. (2024). A time series-based approach to elastic kubernetes scaling. Electronics, 13(2):285.
Benmerar, T. Z., Theodoropoulos, T., Fevereiro, D., Rosa, L., Rodrigues, J., Taleb, T., Barone, P., Tserpes, K., and Cordeiro, L. (2023). Intelligent multi-domain edge orchestration for highly distributed immersive services: an immersive virtual touring use case. In 2023 IEEE International Conference on Edge Computing and Communications (EDGE), pages 381–392. IEEE.
Cheng, K., Zhang, S., Tu, C., Shi, X., Yin, Z., Lu, S., Liang, Y., and Gu, Q. (2023). Proscale: Proactive autoscaling for microservice with time-varying workload at the edge. IEEE Transactions on Parallel and Distributed Systems, 34(4):1294–1312.
Dimolitsas, I., Spatharakis, D., Dechouniotis, D., Zafeiropoulos, A., and Papavassiliou, S. (2023). Multi-application hierarchical autoscaling for kubernetes edge clusters. In 2023 IEEE International Conference on Smart Computing (SMARTCOMP), pages 291–296. IEEE.
Dogani, J., Namvar, R., and Khunjush, F. (2023). Auto-scaling techniques in container-based cloud and edge/fog computing: Taxonomy and survey. Computer Communications, 209:120–150.
Gonçalves, A. L. d. J., Oliveira-Jr, A., and Freitas, L. A. (2023). Revisao sistemática das aplicaçoes imersivas com base nas tecnologias habilitadoras b5g/6g, mec e ia. Anais da XI Escola Regional de Informática de Goiás.
Grafana, 2024. Grafana: The open and composable observability platform. [link]. Acesso em 21 de janeiro de 2024.
Han, B., Pathak, P., Chen, S., and Yu, L.-F. C. (2022). Comic: A collaborative mobile immersive computing infrastructure for conducting multi-user xr research. IEEE Network.
Hubs Foundation (2024). Hubs foundation - open source social vr platform. Accessed: 2024-07-12.
k6.io (2024). k6 Open Source: A modern load testing tool for developers. Acessado em 20 de dezembro de 2024.
KEDA Project (2024). Keda: Kubernetes-based event driven autoscaler. Acessado em: 10-07-2024.
Kubernetes (2022). Kubernetes documentation.
LABORA-INF-UFG (2025). Repositório do projeto adaptscale. [link].
López-Ramírez, G. A., Aragón-Zavala, A., and Vargas-Rosales, C. (2024). Exploratory data analysis for path loss measurements: Unveiling patterns and insights before machine learning. IEEE Access.
Malburg, L., Hoffmann, M., and Bergmann, R. (2023). Applying mape-k control loops for adaptive workflow management in smart factories. Journal of Intelligent Information Systems, 61(1):83–111.
Mehta, R., Sahni, J., and Khanna, K. (2023). Task scheduling for improved response time of latency sensitive applications in fog integrated cloud environment. Multimedia Tools and Applications, 82(21):32305–32328.
Nguyen, T.-T., Yeom, Y.-J., Kim, T., Park, D.-H., and Kim, S. (2020). Horizontal pod autoscaling in kubernetes for elastic container orchestration. Sensors, 20(16):4621.
Pandas (2025). Pandas: Python data analysis library. [link]. Accessed: 2025-01-13.
Pelle, I., Czentye, J., Dóka, J., and Sonkoly, B. (2019). Towards latency sensitive cloud native applications: A performance study on aws. In 2019 IEEE 12th International Conference on Cloud Computing (CLOUD), pages 272–280. IEEE.
Prometheus, T. (Acesso em 21 de janeiro de 2024). Prometheus: The Comprehensive Monitoring and Alerting Toolkit. [link].
Quattrocchi, G., Incerto, E., Pinciroli, R., Trubiani, C., and Baresi, L. (2024). Autoscaling solutions for cloud applications under dynamic workloads. IEEE Transactions on Services Computing.
Santos, J., Wauters, T., Volckaert, B., and De Turck, F. (2023). gym-hpa: Efficient auto-scaling via reinforcement learning for complex microservice-based applications in kubernetes. In NOMS 2023-2023 IEEE/IFIP Network Operations and Management Symposium, pages 1–9. IEEE.
Siriwardhana, Y., Porambage, P., Liyanage, M., and Ylianttila, M. (2021). A survey on mobile augmented reality with 5g mobile edge computing: Architectures, applications, and technical aspects. IEEE Communications Surveys & Tutorials, 23(2):1160–1192.
Taleb, T., Boudi, A., Rosa, L., Cordeiro, L., Theodoropoulos, T., Tserpes, K., Dazzi, P., Protopsaltis, A. I., and Li, R. (2022). Toward supporting xr services: Architecture and enablers. IEEE Internet of Things Journal, 10(4):3567–3586.
Wu, D., Yang, Z., Zhang, P., Wang, R., Yang, B., and Ma, X. (2023). Virtual-reality inter-promotion technology for metaverse: A survey. IEEE Internet of Things Journal.
Yuan, H. and Liao, S. (2024). A time series-based approach to elastic kubernetes scaling. Electronics, 13(2):285.
Publicado
19/05/2025
Como Citar
GONÇALVES, André Luiz de J.; FREITAS, Leandro A.; OLIVEIRA-JR, Antonio.
Arquitetura de Dimensionamento Adaptativo com Suporte ao Aprendizado. In: SIMPÓSIO BRASILEIRO DE REDES DE COMPUTADORES E SISTEMAS DISTRIBUÍDOS (SBRC), 43. , 2025, Natal/RN.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 350-363.
ISSN 2177-9384.
DOI: https://doi.org/10.5753/sbrc.2025.5933.
