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DataLab as a Service: Distributed Computing Framework for Multi-Interactive Analysis Environments
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DOI：10.1109/ACCESS.2025.3536637.png)
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Abstract
En 中文
During the current data era, data analysis across multiple disciplines has become a critical task for researchers to obtain meaningful insights and solve complex problems that are immeasurable using traditional technologies. Big Data has led to the development of state-of-the-art technologies that have revolutionized the process of experimentation. These innovations span from automating the setup of the infrastructure required for data analysis to providing user-friendly interfaces that simplify coding and result visualization. However, managing and scaling these resources for large-scale data processing remains a challenge. In this work, we introduce a novel framework called Datalab as a Service which integrates cutting-edge and open-source technologies to offer an online platform designed for both resource providers and researchers. The platform enables users to easily and automatically deploy interactive environments tailored for data analysis, thereby streamlining the process of managing computational resources. Through DLaaS, users gain access to cloud-based infrastructures and distributed computing resources, which are essential for performing compute-intensive tasks on massive datasets. The framework ensures scalability, resource management and optimization, and high availability, all within an accessible and user-friendly platform. Furthermore, this paper presents several use cases where researchers have successfully utilized DLaaS resources, demonstrating its practical applications in real-world scenarios.
Keywords:
Cloud computing
Microservice architectures
Ecosystems
Computer architecture
Servers
Scalability
Libraries
Complexity theory
Codes
Sparks
Distributed computing
Kubernetes
interactive environments
cloud computing
Jupyter notebooks
Journal
IF:
3.6
Papers:
9.7W
Citations:
29.4W