An optimal workflow scheduling in IoT-fog-cloud system for minimizing time and energy.

Area of Science:

• Computer Science
• Cloud Computing
• Internet of Things

Background:

• Increasing use of Internet of Things (IoT) necessitates efficient processing of workflows.
• Processing IoT workflows on computing platforms increases costs and energy consumption.
• Existing methods struggle to balance energy efficiency and workflow completion time.

Purpose of the Study:

• To address the workflow scheduling problem for IoT devices in fog-cloud environments.
• To reduce energy consumption (EC) and makespan time (MST) of workflows.
• To meet constraints of priority, deadline, and reliability.

Main Methods:

• Utilized a hybrid Aquila and Salp Swarm Algorithm (ASSA) for Virtual Machine (VM) selection.
• Applied the Reducing MakeSpan Time (RMST) technique to minimize workflow execution time.
• Integrated VM merging and Dynamic Voltage Frequency Scaling (DVFS) to reduce static and dynamic energy consumption.

Main Results:

• The proposed ASSA-based method effectively selects optimal VMs for workflow execution.
• RMST technique successfully reduced makespan time while maintaining reliability and deadlines.
• VM merging and DVFS significantly decreased both static and dynamic energy consumption.

Conclusions:

• The experimental results demonstrate the superior effectiveness of the proposed method over existing approaches.
• The study successfully optimizes IoT workflow scheduling in fog-cloud environments for energy efficiency and performance.
• The integrated approach offers a promising solution for sustainable and efficient IoT computing.