Container Performance Prediction: Challenges and Solutions
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Publisher
Springer International Publishing
Link
https://link.springer.com/content/pdf/10.1007/978-3-030-67720-6_27
Reference28 articles.
1. Bao, L., Wu, C., Bu, X., Ren, N., Shen, M.: Performance modeling and workflow scheduling of microservice-based applications in clouds. IEEE Trans. Parallel Distrib. Syst. 30(9), 2114–2129 (2019)
2. Bauer, A., Lesch, V., Versluis, L., Ilyushkin, A., Herbst, N., Kounev, S.: Chamulteon: coordinated auto-scaling of micro-services. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 2015–2025. IEEE (2019)
3. Baughman, M., Chard, R., Ward, L.T., Pitt, J., Chard, K., Foster, I.T.: Profiling and predicting application performance on the cloud. In: UCC, pp. 21–30 (2018)
4. Boza, E.F., Abad, C.L., Narayanan, S.P., Balasubramanian, B., Jang, M.: A case for performance-aware deployment of containers. In: Proceedings of the 5th International Workshop on Container Technologies and Container Clouds, pp. 25–30 (2019)
5. Callara, M., Wira, P.: A probabilistic learning approach for predicting application launches in cloud computing architectures. In: 2019 IEEE/SICE International Symposium on System Integration (SII), pp. 584–589. IEEE (2019)
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