[2307.09132] Cloud-native RStudio on Kubernetes for Hopsworks
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Computer Science > Distributed, Parallel, and Cluster Computing

arXiv:2307.09132 (cs)
[Submitted on 18 Jul 2023]

Title:Cloud-native RStudio on Kubernetes for Hopsworks

Authors:Gibson Chikafa, Sina Sheikholeslami, Salman Niazi, Jim Dowling, Vladimir Vlassov
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Abstract: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.
Comments: 8 pages, 4 figures
Subjects: Distributed, Parallel, and Cluster Computing (cs.DC); Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
Cite as: arXiv:2307.09132 [cs.DC]
  (or arXiv:2307.09132v1 [cs.DC] for this version)
  https://doi.org/10.48550/arXiv.2307.09132

Submission history

From: Vladimir Vlassov [view email]
[v1] Tue, 18 Jul 2023 10:28:55 UTC (719 KB)
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