In order to fully benefit from cloud computing, services are designed
following the "multi-tenant" architectural model, which is aimed at maximizing
resource sharing among users. However, multi-tenancy introduces challenges of
security, performance isolation, scaling, and customization. RStudio server is
an open-source Integrated Development Environment (IDE) accessible over a web
browser for the R programming language. We present the design and
implementation of a multi-user distributed system on Hopsworks, a
data-intensive AI platform, following the multi-tenant model that provides
RStudio as Software as a Service (SaaS). We use the most popular cloud-native
technologies: Docker and Kubernetes, to solve the problems of performance
isolation, security, and scaling that are present in a multi-tenant
environment. We further enable secure data sharing in RStudio server instances
to provide data privacy and allow collaboration among RStudio users. We
integrate our system with Apache Spark, which can scale and handle Big Data
processing workloads. Also, we provide a UI where users can provide custom
configurations and have full control of their own RStudio server instances. Our
system was tested on a Google Cloud Platform cluster with four worker nodes,
each with 30GB of RAM allocated to them. The tests on this cluster showed that
44 RStudio servers, each with 2GB of RAM, can be run concurrently. Our system
can scale out to potentially support hundreds of concurrently running RStudio
servers by adding more resources (CPUs and RAM) to the cluster or system.

使用 Docker 和 Kubernetes 等云原生技术，在 Hopsworks 平台上实现了一个基于多租户模型的 RStudio 服务，解决了性能隔离、安全性和扩展性等多租户环境中的问题，并集成了 Apache Spark 进行大数据处理工作负载，可支持同时运行数百个 RStudio 服务器的潜力。