Visible to the public CRA: Enabling Data-Intensive Applications in Containerized Environments

TitleCRA: Enabling Data-Intensive Applications in Containerized Environments
Publication TypeConference Paper
Year of Publication2019
AuthorsSabek, I., Chandramouli, B., Minhas, U. F.
Conference Name2019 IEEE 35th International Conference on Data Engineering (ICDE)
Keywordscloud computing, cloud-edge applications, cloud-scale data centers, common runtime for applications, compositionality, computer centres, containerization, containerization technologies, Containers, data centers, Data-Intensive Applications, dataflow processing, distributed, Docker, generic dataflow layer, Kubernetes, Kubernetes/Docker, Libraries, metadata, Metadata Discovery Problem, pubcrawl, resilience, Resiliency, resource orchestration capabilities, Runtime, Scalability
AbstractToday, a modern data center hosts a wide variety of applications comprising batch, interactive, machine learning, and streaming applications. In this paper, we factor out the commonalities in a large majority of these applications, into a generic dataflow layer called Common Runtime for Applications (CRA). In parallel, another trend, with containerization technologies (e.g., Docker), has taken a serious hold on cloud-scale data centers, with direct implications on building next generation of data center applications. Container orchestrators (e.g., Kubernetes) have made deployment a lot easy, and they solve many infrastructure level problems, e.g., service discovery, auto-restart, and replication. For best in class performance, there is a need to marry the next generation applications with containerization technologies. To that end, CRA leverages and builds upon the containerization and resource orchestration capabilities of Kubernetes/Docker, and makes it easy to build a wide range of cloud-edge applications on top. To the best of our knowledge, we are the first to present a cloud native runtime for building data center applications. We show the efficiency of CRA through various micro-benchmarking experiments.
Citation Keysabek_cra_2019