Projekt per år
Dynamic resource management is a difficult problem in modern microservice applications. Many proposed methods rely on the availability of an analytical performance model, often based on queueing theory. Such models can always be hand-crafted, but this takes time and requires expert knowledge. Various methods have been proposed that can automatically extract models from logs or tracing data. However, they are often intricate, requiring off-line stages and advanced algorithms for retrieving the service-time distributions. Furthermore, the resulting models can be complex and unsuitable for online evaluation. Aiming for simplicity, we in this paper introduce a general queuing network model for microservice applications that can be (i) quickly and accurately solved using a refined mean-field fluid model and (ii) completely extracted at runtime in a distributed fashion from common local tracing data at each service. The fit of the model and the prediction accuracies under system perturbations are evaluated in a cloud-based microservice application and are found to be accurate.
|Titel på värdpublikation||2022 IEEE 15th International Conference on Cloud Computing (CLOUD)|
|Förlag||IEEE - Institute of Electrical and Electronics Engineers Inc.|
|Status||Published - 2022 juli|
|Evenemang|| 2022 IEEE 15th International Conference on Cloud Computing (CLOUD) - Barcelona, Spain, Barcelona, Spanien|
Varaktighet: 2022 juli 11 → 2022 juli 15
|Konferens||2022 IEEE 15th International Conference on Cloud Computing (CLOUD)|
|Period||2022/07/11 → 2022/07/15|
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- 1 Avslutade
Event-Based Information Fusion for the Self-Adaptive Cloud
Ruuskanen, J., Cervin, A. & Årzén, K.
2017/09/01 → 2022/12/01