Energy-efficient Software-defined 5G/6G Multimedia IoV: PID controller-based approach

TL;DR

This paper introduces an energy-efficient, PID controller-based framework for multimedia resource management in 5G/6G IoV networks, integrating SDN and NFV to enhance load balancing and reduce energy consumption.

Contribution

It presents a novel, adaptive control framework using PID controllers for energy-efficient resource management in software-defined 5G/6G IoV networks, validated through comprehensive simulations.

Findings

Reduced power consumption by up to 30% under heavy traffic

Achieved nearly equal load distribution across network components

Responded to over 98% of vehicle requests in extreme traffic scenarios

Abstract

The rapid proliferation of multimedia applications in smart city environments and the Internet of Vehicles (IoV) presents significant challenges for existing network infrastructures, particularly with the advent of 5G and emerging 6G technologies. Traditional architectures struggle to meet the demands for scalability, adaptability, and energy efficiency required by data-intensive multimedia services. To address these challenges, this study proposes an innovative, energy-efficient framework for multimedia resource management in software-defined 5G/6G IoV networks, leveraging a Proportional-Integral-Derivative (PID) controller. The framework integrates Software-Defined Networking (SDN) and Network Functions Virtualization (NFV) technologies to enable centralized and adaptive control over network resources. By employing a PID controller, it dynamically manages load distribution and temperature, ensuring balanced resource allocation and minimizing energy waste. Comprehensive simulations validate the framework's effectiveness, demonstrating significant improvements in load balancing, CPU utilization, and energy consumption compared to traditional methods. For instance, under heavy traffic conditions, the proposed framework maintained resource efficiency, reducing power consumption by up to 30% and achieving nearly equal load distribution across all network components. Additionally, the controller exhibited exceptional scalability, effectively responding to over 98% of vehicle requests even in scenarios of extreme traffic demand.