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EEOA: Cost and Energy Efficient Task Scheduling in a Cloud-Fog Framework.

M Santhosh Kumar1, Ganesh Reddy Karri1

  • 1School of Computer Science and Engineering, VIT-AP University, Amaravathi 522237, Andhra Pradesh, India.

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|March 11, 2023
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Summary
This summary is machine-generated.

A new electric earthworm optimization algorithm (EEOA) efficiently schedules Internet of Things (IoT) tasks in cloud-fog systems. This method significantly improves efficiency, reduces energy consumption, and lowers costs compared to existing algorithms.

Keywords:
CEA-CURIEHPC2Nearthworm optimization algorithmelectric fish optimizationinternet of things

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Area of Science:

  • Cloud-fog computing
  • Internet of Things (IoT)
  • Task Scheduling Algorithms

Background:

  • The rapid growth of IoT generates massive data, necessitating efficient task scheduling in cloud-fog environments.
  • Existing scheduling methods often neglect crucial factors like energy consumption and cost.
  • There is a need for advanced algorithms to manage heterogeneous workloads and enhance Quality of Service (QoS).

Purpose of the Study:

  • To propose a novel multi-objective task scheduling algorithm for IoT requests within a cloud-fog framework.
  • To address limitations of existing methods by incorporating energy efficiency and cost-effectiveness.
  • To enhance the overall efficiency and QoS of cloud-fog services.

Main Methods:

  • Developed the electric earthworm optimization algorithm (EEOA) by combining the Earthworm Optimization Algorithm (EOA) and Electric Fish Optimization (EFO).
  • Designed EEOA to optimize task scheduling considering execution time, cost, makespan, and energy consumption.
  • Evaluated the algorithm using real-world workloads like CEA-CURIE and HPC2N.

Main Results:

  • The proposed EEOA demonstrated significant improvements: 89% increase in efficiency, 94% reduction in energy consumption, and 87% decrease in total cost.
  • Performance was validated against existing algorithms across various benchmarks.
  • Simulation results confirm the superiority of EEOA in optimizing cloud-fog task scheduling.

Conclusions:

  • The electric earthworm optimization algorithm (EEOA) offers a superior scheduling scheme for IoT tasks in cloud-fog environments.
  • EEOA effectively balances performance metrics including efficiency, energy usage, and cost.
  • This research provides a promising solution for optimizing resource management in the era of big data and IoT.