Comparison of Microservice Call Rate Predictions for Replication in the Cloud

Autor:	Mehran, Narges, Haghighi, Arman, Aminharati, Pedram, Nikolov, Nikolay, Soylu, Ahmet, Roman, Dumitru, Prodan, Radu
Rok vydání:	2023
Předmět:	Computer Science - Distributed Parallel and Cluster Computing Computer Science - Artificial Intelligence Computer Science - Machine Learning Computer Science - Neural and Evolutionary Computing
Druh dokumentu:	Working Paper
Popis:	Today, many users deploy their microservice-based applications with various interconnections on a cluster of Cloud machines, subject to stochastic changes due to dynamic user requirements. To address this problem, we compare three machine learning (ML) models for predicting the microservice call rates based on the microservice times and aiming at estimating the scalability requirements. We apply the linear regression (LR), multilayer perception (MLP), and gradient boosting regression (GBR) models on the Alibaba microservice traces. The prediction results reveal that the LR model reaches a lower training time than the GBR and MLP models. However, the GBR reduces the mean absolute error and the mean absolute percentage error compared to LR and MLP models. Moreover, the prediction results show that the required number of replicas for each microservice by the gradient boosting model is close to the actual test data without any prediction. Comment: 7 pages, 5 figures, 4 tables
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2401.03319 Zobrazit plný text záznamu View this record from Arxiv