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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.
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.
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.
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