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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

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Swarm intelligence-based packet scheduling for future intelligent networks.

Arif Husen^1,2, Muhammad Hasanain Chaudary¹, Farooq Ahmad¹

¹Department of Computer Science, COMSATS University Islamabad, Lahore, Punjab, Pakistan.

Peerj. Computer Science

|December 11, 2023

Summary

This summary is machine-generated.

This study introduces a machine learning approach for intelligent packet scheduling in networks. The proposed deep learning model automates decisions, improving network performance and cost-effectiveness.

Keywords:

Data mining Emerging technologies Machine learning TIPS

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Area of Science:

Computer Science
Artificial Intelligence
Network Engineering

Background:

Network operations rely on decentralized decision-making for functions like traffic scheduling and policy enforcement.
Human intervention in network decisions leads to high costs, delays, and errors.
Machine learning (ML) and artificial intelligence (AI) offer intelligent automation for network functions.

Purpose of the Study:

To investigate state-of-the-art packet scheduling methods.
To propose a novel ML-based approach for agile and cost-effective packet scheduling.
To address challenges in current network decision-making processes.

Main Methods:

Literature review of current packet scheduling and decision processes.
Development and implementation of a deep learning-based model for packet scheduling.
Experimental analysis of the proposed model's performance.

Main Results:

The proposed deep learning model effectively automates packet scheduling decisions.
The model achieved high network performance metrics: 99.95% throughput, 99.97% delay, and 99.94% jitter.
Performance significantly surpassed traditional static traffic profile configurations.

Conclusions:

ML-based packet scheduling offers an agile and cost-effective solution for intelligent networks.
The proposed deep learning approach successfully addresses network operational challenges.
Intelligent automation through ML/AI is crucial for future network advancements.