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Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

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An Intelligent Proposed Model for Task Offloading in Fog-Cloud Collaboration Using Logistics Regression.

Muhammad Mazhar Bukhari¹, Taher M Ghazal^2,3, Sagheer Abbas¹

¹Department of Computer Science, National College of Business Administration and Economics, Lahore 54660, Pakistan.

Computational Intelligence and Neuroscience

|February 7, 2022

Summary

Intelligent systems face challenges with centralized cloud computing. This research proposes a dynamic task offloading model using fog-cloud collaboration to improve quality of service for delay-sensitive applications.

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Area of Science:

Computer Science
Distributed Systems
Artificial Intelligence

Background:

Modern smart applications and intelligent systems require decentralized computing solutions.
Centralized cloud models are impractical for numerous connected devices and delay-sensitive applications due to latency and bandwidth limitations.
Fog computing offers edge services but has limited resources, necessitating collaboration with cloud computing.

Purpose of the Study:

To address the complexity of dynamic task offloading decisions in fog-cloud environments.
To propose an intelligent model for optimizing task offloading between fog and cloud resources.
To enhance Quality of Service (QoS) for delay-sensitive applications through efficient resource utilization.

Main Methods:

Development of a novel intelligent task offloading model.
Utilizing logistic regression for predictive task offloading policy.
Simulation-based evaluation of the proposed model against other algorithms.

Main Results:

The proposed logistic regression model achieved 86% accuracy in predictive task offloading.
Demonstrated the model's ability to ensure process consistency and reliability.
Validated the effectiveness of fog-cloud collaboration for dynamic task offloading.

Conclusions:

Fog-cloud collaboration is essential for managing dynamic task offloading in intelligent systems.
The proposed intelligent model provides an accurate and reliable solution for task offloading decisions.
This approach significantly improves QoS for delay-sensitive applications by optimizing resource allocation.