Author Identifier

Muhammad Waqas: https://orcid.org/0000-0003-0814-7544

Document Type

Journal Article

Publication Title

PLoS ONE

Volume

19

Issue

12

PubMed ID

39666605

Publisher

PLOS

School

School of Engineering

RAS ID

76652

Comments

Bano, S., Abbas, G., Bilal, M., Abbas, Z. H., Ali, Z., & Waqas, M. (2024). PHyPO: Priority-based hybrid task partitioning and offloading in mobile computing using automated machine learning. PloS one, 19(12). https://doi.org/10.1371/journal.pone.0314198

Abstract

With the increasing demand for mobile computing, the requirement for intelligent resource management has also increased. Cloud computing lessens the energy consumption of user equipment, but it increases the latency of the system. Whereas edge computing reduces the latency along with the energy consumption, it has limited resources and cannot process bigger tasks. To resolve these issues, a Priority-based Hybrid task Partitioning and Offloading (PHyPO) scheme is introduced in this paper, which prioritizes the tasks with high time sensitivity and offloads them intelligently. It also calculates the optimal number of partitions a task can be divided into. The utility of resources is maximized along with increasing the processing capability of the model by using a hybrid architecture, consisting of mobile devices, edge servers, and cloud servers. Automated machine learning is used to identify the optimal classification models, along with tuning their hyper-parameters, which results in adaptive boosting ensemble learning-based models to reduce the time complexity of the system to O(1). The results of the proposed algorithm show a significant improvement over benchmark techniques along with achieving an accuracy of 96.1% for the optimal partitioning model and 94.3% for the optimal offloading model, with both the results being achieved in significantly less or equal time as compared to the benchmark techniques.

DOI

10.1371/journal.pone.0314198

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

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