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PHyPO: Priority-based Hybrid task Partitioning and Offloading in mobile computing using automated machine learning.

Shehr Bano^1,2, Ghulam Abbas^2,3, Muhammad Bilal⁴

¹School of Computing and Engineering, University of Huddersfield, Huddersfield, United Kingdom.

|December 12, 2024

Summary

This summary is machine-generated.

A new Priority-based Hybrid task Partitioning and Offloading (PHyPO) scheme optimizes mobile computing by intelligently partitioning and offloading tasks. This hybrid approach enhances resource utility and processing power for mobile devices, edge, and cloud servers.

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Area of Science:

Computer Science
Artificial Intelligence
Mobile Computing

Background:

Mobile computing demands intelligent resource management.
Cloud computing increases latency; edge computing has limited resources.
Existing solutions struggle with balancing latency, energy, and resource constraints.

Purpose of the Study:

To introduce a Priority-based Hybrid task Partitioning and Offloading (PHyPO) scheme.
To address the limitations of cloud and edge computing for mobile tasks.
To enhance resource utilization and processing capabilities in mobile environments.

Main Methods:

Developed a hybrid architecture integrating mobile devices, edge, and cloud servers.
Implemented a prioritization mechanism for time-sensitive tasks.
Utilized automated machine learning for optimal model selection and hyper-parameter tuning.
Calculated optimal task partitioning and offloading strategies.

Main Results:

Achieved 96.1% accuracy for optimal partitioning and 94.3% for optimal offloading.
Demonstrated significant improvements over benchmark techniques.
Reduced system time complexity to O(1) using adaptive boosting ensemble learning.
Maximized resource utility and processing capability.

Conclusions:

The PHyPO scheme effectively manages mobile computing resources.
The hybrid architecture and intelligent offloading significantly improve performance.
Automated machine learning integration leads to efficient and adaptive task processing.