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An Efficient Dynamic-Decision Based Task Scheduler for Task Offloading Optimization and Energy Management in Mobile

Abid Ali1, Muhammad Munawar Iqbal1, Harun Jamil2

  • 1Department of Computer Science, University of Engineering and Technology, Taxila 47080, Pakistan.

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Summary
This summary is machine-generated.

This study introduces a novel algorithm for mobile devices to optimize energy and time management during task processing. The new method enhances computational performance and reduces energy consumption for mobile cloud computing tasks.

Keywords:
cloud virtual machinesfault tolerancemobile cloud computingoffloadingtask scheduling

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Area of Science:

  • Computer Science
  • Mobile Computing
  • Distributed Systems

Background:

  • Mobile devices face limitations in storage, computation, and energy, impacting task processing efficiency.
  • These limitations are critical in mobile cloud computing, fog computing, and edge computing environments.
  • Task offloading and management are complex due to mobile devices' inherent resource constraints.

Purpose of the Study:

  • To develop an energy-efficient dynamic decision-based task scheduling algorithm for mobile devices.
  • To enhance mobile device decision-making and computational performance during task offloading.
  • To reduce energy consumption and execution time for mobile computing tasks.

Main Methods:

  • A novel task scheduling algorithm based on an energy-efficient dynamic decision-making approach.
  • Implementation of a dedicated task scheduling server for cloud-based offloading computation.
  • Empirical algorithm application for optimizing task scheduling processes.

Main Results:

  • The proposed model demonstrates rapid adaptation to cloud computing tasks and mobile device resource computations.
  • Significant reductions in energy consumption and task scheduling time were achieved.
  • Enhanced mobile device decision-making ability and computational performance during task offloading.

Conclusions:

  • The developed algorithm effectively addresses energy consumption and time execution challenges in mobile computing.
  • The novel task scheduling server improves overall system efficiency and mobile device capabilities.
  • This research offers a practical solution for optimizing resource management in mobile cloud environments.