Abstract
The categories of programs running in data centers are changing from the traditional monolithic program to loosely coupled microservice programs. Microservice programs are easy to update and can facilitate software heterogeneity. However, microservice programs have more stringent performance requirements. Estimating the resource requirements of each service becomes the key point to ensuring the QoS of the microservice programs. In this paper, we propose a QoS-aware framework Adjust for microservice programs. Adjust establishes a neural network-based microservice QoS prediction model. Moreover, Adjust identifies the causes of the abnormality of microservice programs, and determines performance assurance strategies on both system and microservice levels. By dynamically adjusting the resource allocation, Adjust can effectively guarantee the performance of microservice programs and improves system resource utilization at the same time.
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Wang, L., Huang, T., Geng, S., Li, D., Zhu, X., Zhang, H. (2022). Adjust: An Online Resource Adjustment Framework for Microservice Programs. In: Liu, S., Wei, X. (eds) Network and Parallel Computing. NPC 2022. Lecture Notes in Computer Science, vol 13615. Springer, Cham. https://doi.org/10.1007/978-3-031-21395-3_11
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DOI: https://doi.org/10.1007/978-3-031-21395-3_11
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