State of the Art on Microservices Autoscaling: An Overview
Resumo
The adoption of microservices architecture has taken on great pro-portions due to its benefits and popularization of containers driven tools, such as Kubernetes and Docker. Besides, the development of microservice-based applications is a complex task, specially because they can be composed of multiple heterogeneous parts. In particular, one of the main challenges is how to conduct the microservices autoscaling (i.e., adding or removing resources on demand), while still avoiding resource waste, such as CPU and memory. This paper presents the state of the art of approaches to solve the problem of micro services autoscaling, the main characteristics to be considered as well as the important future directions that need to be still investigated.
Palavras-chave:
microservices, Autoscaling, kubernetes, docker, container
Referências
Abdel Khaleq, A. and Ra, I. (2019). Agnostic approach for microservices autoscaling incloud applications. InCSCI, pages 1411–1415.
Calcavecchia, N., Caprarescu, B., Di Nitto, E., Dubois, D., and Petcu, D. (2012). Depas:A decentralized probabilistic algorithm for auto-scaling.Computing, 94.
Cerqueira De Abranches, M. and Solis, P. (2016). An algorithm based on response timeand traffic demands to scale containers on a cloud computing system. InNCA, pages343–350.
Felizardo, K., Nakagawa, E., Fabbri, S., and Ferrari, F. (2017).Systematic LiteratureReview in Software Engineering: Theory and Practice. Elsevier Brazil (in Portuguese).
Fourati, M. H., Marzouk, S., Drira, K., and Jmaiel, M. (2019). Dockeranalyzer: To-wards fine grained resource elasticity for microservices-based applications deployedwith Docker. InPDCAT, pages 220–225.
Guerrero, C., Lera, I., and Juiz, C. (2017). Genetic algorithm for multi-objective opti-mization of container allocation in cloud architecture.Journal of Grid Computing,16(1):113–135.
Imdoukh, M., Ahmad, I., and Alfailakawi, M. G. (2020).Machine learning-basedauto-scaling for containerized applications.Neural Computing and Applications,32(13):9745–9760.
Kampars, J. and Pinka, K. (2017). Auto-scaling and adjustment platform for cloud-basedsystems. InISPC, pages 52–57.
Kitchenham, B., Budgen, D., and Brereton, O. (2015).Evidence-Based Software Engi-neering and Systematic Reviews. CRC Press.
Kwan, A., Wong, J., Jacobsen, H., and Muthusamy, V. (2019). Hyscale: Hybrid andnetwork scaling of dockerized microservices in cloud data centres. InICDCS, pages80–90.
Lorido-Botran, T., Miguel-Alonso, J., and Lozano, J. (2014). A review of auto-scalingtechniques for elastic applications in cloud environments.Journal of Grid Computing,12(4):559–592
ópez, M. and Spillner, J. (2017). Towards quantifiable boundaries for elastic horizontalscaling of microservices. InUCC, pages 35–40.
Nguyen, T.-T., Yeom, Y.-J., Kim, T., Park, D.-H., and Kim, S. (2020). Horizontal podautoscaling in kubernetes for elastic container orchestration.Sensors (Switzerland),20(16):1–18.
Rzadca, K. and et al. (2020). Autopilot: Workload autoscaling at Google. InEuroSys,pages 1–16.
Tönes, J. (2015). Microservices.IEEE Software, 32(1):116–116.
Ye, T., Guangtao, X., Shiyou, Q., and Minglu, L. (2017). An auto-scaling framework forcontainerized elastic applications. InBigCom, pages 422–430.
Zhao, H., Lim, H., Hanif, M., and Lee, C. (2019). Predictive container auto-scaling forcloud-native applications. InICTC, pages 1280–1282.
Zhou, X., Jin, Y., Zhang, H., Li, S., and Huang, X. (2016). A map of threats to validity ofsystematic literature reviews in software engineering. InAPSEC, pages 153–160.
Calcavecchia, N., Caprarescu, B., Di Nitto, E., Dubois, D., and Petcu, D. (2012). Depas:A decentralized probabilistic algorithm for auto-scaling.Computing, 94.
Cerqueira De Abranches, M. and Solis, P. (2016). An algorithm based on response timeand traffic demands to scale containers on a cloud computing system. InNCA, pages343–350.
Felizardo, K., Nakagawa, E., Fabbri, S., and Ferrari, F. (2017).Systematic LiteratureReview in Software Engineering: Theory and Practice. Elsevier Brazil (in Portuguese).
Fourati, M. H., Marzouk, S., Drira, K., and Jmaiel, M. (2019). Dockeranalyzer: To-wards fine grained resource elasticity for microservices-based applications deployedwith Docker. InPDCAT, pages 220–225.
Guerrero, C., Lera, I., and Juiz, C. (2017). Genetic algorithm for multi-objective opti-mization of container allocation in cloud architecture.Journal of Grid Computing,16(1):113–135.
Imdoukh, M., Ahmad, I., and Alfailakawi, M. G. (2020).Machine learning-basedauto-scaling for containerized applications.Neural Computing and Applications,32(13):9745–9760.
Kampars, J. and Pinka, K. (2017). Auto-scaling and adjustment platform for cloud-basedsystems. InISPC, pages 52–57.
Kitchenham, B., Budgen, D., and Brereton, O. (2015).Evidence-Based Software Engi-neering and Systematic Reviews. CRC Press.
Kwan, A., Wong, J., Jacobsen, H., and Muthusamy, V. (2019). Hyscale: Hybrid andnetwork scaling of dockerized microservices in cloud data centres. InICDCS, pages80–90.
Lorido-Botran, T., Miguel-Alonso, J., and Lozano, J. (2014). A review of auto-scalingtechniques for elastic applications in cloud environments.Journal of Grid Computing,12(4):559–592
ópez, M. and Spillner, J. (2017). Towards quantifiable boundaries for elastic horizontalscaling of microservices. InUCC, pages 35–40.
Nguyen, T.-T., Yeom, Y.-J., Kim, T., Park, D.-H., and Kim, S. (2020). Horizontal podautoscaling in kubernetes for elastic container orchestration.Sensors (Switzerland),20(16):1–18.
Rzadca, K. and et al. (2020). Autopilot: Workload autoscaling at Google. InEuroSys,pages 1–16.
Tönes, J. (2015). Microservices.IEEE Software, 32(1):116–116.
Ye, T., Guangtao, X., Shiyou, Q., and Minglu, L. (2017). An auto-scaling framework forcontainerized elastic applications. InBigCom, pages 422–430.
Zhao, H., Lim, H., Hanif, M., and Lee, C. (2019). Predictive container auto-scaling forcloud-native applications. InICTC, pages 1280–1282.
Zhou, X., Jin, Y., Zhang, H., Li, S., and Huang, X. (2016). A map of threats to validity ofsystematic literature reviews in software engineering. InAPSEC, pages 153–160.
Publicado
18/07/2021
Como Citar
NUNES, João Paulo K. S.; BIANCHI, Thiago; IWASAKI, Anderson Y.; NAKAGAWA, Elisa Yumi.
State of the Art on Microservices Autoscaling: An Overview. In: SEMINÁRIO INTEGRADO DE SOFTWARE E HARDWARE (SEMISH), 48. , 2021, Evento Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 30-38.
ISSN 2595-6205.
DOI: https://doi.org/10.5753/semish.2021.15804.
