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This paper introduces recent advances in EnOSlib, a Python library that aims at facilitating the design and execution of reproducible experiments across distributed computing infrastructures. Originally developed to simplify experimentation on testbeds such as Grid’5000 and Chameleon Cloud, EnOSlib now incorporates support multi-provider deployments, including access to edge resources, as well as advanced services. Key contributions include integration with Kwollect for fine-grained energy measurements, a planning service for executing timed events, and enhanced network emulation functionalities. These features enable users to model and study complex, realistic scenarios such as latency-sensitive edge-to-cloud applications. A major new capability is the support for synchronized multi-infrastructure experiments, allowing simultaneous resource reservation and deployment across diverse testbeds. The paper illustrates these capabilities through a distributed video processing use case spanning edge and cloud platforms. This paper is the companion paper of the tutorial presented in DAIS 2025.

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Multi-provider Capabilities in EnOSlib: Driving Distributed System Experiments on the Edge-to-Cloud Continuum

  • 会议论文

  • 发表日期：

    2025年

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  • Baptiste Jonglez,
  • Matthieu Simonin,
  • Jolan Philippe,
  • Sidi Mohammed Kaddour

摘要

This paper introduces recent advances in EnOSlib, a Python library that aims at facilitating the design and execution of reproducible experiments across distributed computing infrastructures. Originally developed to simplify experimentation on testbeds such as Grid’5000 and Chameleon Cloud, EnOSlib now incorporates support multi-provider deployments, including access to edge resources, as well as advanced services. Key contributions include integration with Kwollect for fine-grained energy measurements, a planning service for executing timed events, and enhanced network emulation functionalities. These features enable users to model and study complex, realistic scenarios such as latency-sensitive edge-to-cloud applications. A major new capability is the support for synchronized multi-infrastructure experiments, allowing simultaneous resource reservation and deployment across diverse testbeds. The paper illustrates these capabilities through a distributed video processing use case spanning edge and cloud platforms. This paper is the companion paper of the tutorial presented in DAIS 2025.