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This study introduces a novel simulator extension for modeling Internet of Things (IoT) workflows in cloud and fog computing environments. The enhanced simulator addresses limitations in existing tools, improving the simulation of complex IoT applications.

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Area of Science:

  • Computer Science
  • Cloud Computing
  • Internet of Things

Background:

  • Modeling Internet of Things (IoT) applications in cloud and fog computing presents challenges due to complex trigger-based events and workflow compositions.
  • Real-world deployment of IoT workflows faces hurdles like high operational costs and access limitations, necessitating simulation solutions.

Purpose of the Study:

  • To introduce a novel simulator extension, DISSECT-CF-Fog, for modeling IoT use cases by leveraging workflow scheduling and execution capabilities.
  • To address the omission of the IoT factor in state-of-the-art scientific workflow evaluation simulators.
  • To present a scalability study on scientific workflows and their interoperability with IoT workflows within the DISSECT-CF-Fog simulator.

Main Methods:

  • Development of a novel extension for the DISSECT-CF-Fog simulator.
  • Integration of workflow scheduling and execution capabilities to model IoT use cases.
  • Conducting a scalability study focusing on scientific and IoT workflow interoperability.

Main Results:

  • The novel simulator extension effectively models real-life IoT use cases, including complex workflow compositions.
  • The study highlights the limitations of current simulators in evaluating scientific workflows with an IoT component.
  • The DISSECT-CF-Fog simulator demonstrates capabilities for scalability studies and assessing the interoperability of scientific and IoT workflows.

Conclusions:

  • The enhanced DISSECT-CF-Fog simulator provides a valuable tool for modeling and evaluating complex IoT applications and workflows.
  • The research emphasizes the need for simulators that can handle the integration of IoT factors in scientific workflow analysis.
  • The findings support the use of DISSECT-CF-Fog for investigating the scalability and interoperability of diverse workflow types in distributed computing environments.