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(CLOUD 2020) Capestor: a Service Mesh-based Capacity Estimation Framework for Cloud Applications

About This Webinar

Abstract: Due to complex deployment configurations and application logics in cloud computing, microservice architectures are widely used to divide cloud appli-cations into multiple independent microservices communicating with each other through APIs that are not associated with languages and platforms. However, a large amount of various microservices make it difficult for opera-tors to predict the performance of microservices and then estimate cloud ap-plications' capacity. This paper proposes a capacity estimation framework called as Capestor for cloud applications based on a service mesh. Capestor employs a service mesh to place target microservices in isolated containers, simulates workloads, and collect monitoring data related with performance and resources. Then, Capestor employs a lasso regression model to correlate resources and performance, and estimates the capacity of each microservice to plan fine-grained flexible expansion. Finally, we evaluate Capestor with a typical microservice based application. The experimental results show that Capestor can estimate the capacity of microservices, and provide perfor-mance guarantee for applications with low prediction error.

Authors: Yao Sun (Nanjing Institute of Big Date, Jinling Institute of Technology, China); Lun Meng (Hohai University, China); Shudong Zhang (Capital Normal University, China)

Email: myresearch2020@126.com, studyscholar@foxmail.com, zsd@mail.cnu.edu.cn

Who can view: Everyone
Webinar Price: Free
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Lun Meng received the PH.D. degree in Communication from the Tsinghua University in 2013, and M.S. degree in Communication from the Northeast Normal University of China in 2009. She is an assistant professor in the Hohai University. Her research interests include data mining,and public opinion analysis.
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