Comparison of Microservice Call Rate Predictions for Replication in the Cloud
Alpen-Adria-Universität Klagenfurt（阿尔卑斯-亚得里亚大学克拉根福分校）；Azad University, Science and Research Branch, Tehran（阿扎德大学德黑兰科学与研究分校）；SINTEF AS（挪威SINTEF公司）；OsloMet - Oslo Metropolitan University（奥斯陆城市大学）
Narges Mehran, Arman Haghighi, Pedram Aminharati, Nikolay Nikolov, Ahmet Soylu, Dumitru Roman, Radu Prodan
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.
iDynamics: A Configurable Emulation Framework for Evaluating Microservice Scheduling Policies under Controllable Cloud-Edge Dynamics
Quantum Cloud Computing and Distributed Systems (qCLOUDS) Laboratory, School of Computing and Information Systems, The University of Melbourne, Australia（墨尔本大学计算与信息系统学院量子云计算与分布式系统实验室）
Grouper: Scheduling Groups for Multi-Tenant Microsecond-Scale Microservices
Sharif University of Technology（谢里夫理工大学）；Iran University of Science and Technology（伊朗科技大学）；KTH Royal Institute of Technology（皇家理工学院）；Amirkabir University of Technology（阿米尔卡比尔理工大学）
