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A Multi-Classifiers Based Algorithm for Energy Efficient Tasks Offloading in Fog Computing.

Moteb K Alasmari1, Sami S Alwakeel1, Yousef A Alohali1

  • 1College of Computer and Information Sciences, King Saud University, Riyadh 11362, Saudi Arabia.

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Summary
This summary is machine-generated.

Optimizing task offloading for Internet of Things (IoT) devices is crucial. The MCEETO strategy enhances energy efficiency and reduces network usage in hybrid IoT, Fog, and Cloud environments.

Keywords:
energy efficiencyfog computingiFogSiminternet of thingsmodule placementtask offloading

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Area of Science:

  • Computer Science
  • Networking
  • Distributed Systems

Background:

  • Internet of Things (IoT) devices generate vast data, making remote cloud offloading inefficient and costly.
  • Fog Computing offers proximity and lower latency but has limited resources, leading to frequent cloud offloading.
  • Optimizing energy consumption and meeting deadlines for IoT devices in a hybrid environment is a significant challenge.

Purpose of the Study:

  • To explore effective task offloading strategies in Fog Computing environments.
  • To propose an intelligent energy-aware allocation strategy for efficient task offloading in a hybrid IoT, Fog, and Cloud paradigm.
  • To improve the categorization and distribution of IoT device tasks for optimal resource utilization.

Main Methods:

  • The study proposes MCEETO, an intelligent energy-aware allocation strategy using a multi-classifier algorithm.
  • MCEETO selects optimal Fog Devices (FDs) for module placement based on task attributes, Fog node characteristics, network latency, and bandwidth.
  • The iFogSim simulator was used for evaluation and comparison with edge-ward and Cloud-only strategies.

Main Results:

  • MCEETO demonstrated superior energy efficiency, saving approximately 11.36% compared to Cloud-only and 9.30% compared to edge-ward strategies.
  • The MCEETO algorithm achieved significant reductions in network usage: 67% compared to edge-ward and 96% compared to Cloud-only.
  • The strategy effectively optimizes task offloading in a hybrid IoT, Fog, and Cloud setting.

Conclusions:

  • The proposed MCEETO strategy offers a more energy-efficient and network-optimized solution for IoT task offloading.
  • Intelligent, multi-classifier-based allocation strategies are effective in hybrid cloud environments.
  • Further research can explore advanced resource management techniques for heterogeneous IoT ecosystems.