  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.
