Mathematical Modeling and Genetic Algorithm-Based Hyperheuristic Optimization for Quality of Service and Load Balancing in Cloud Communication Networks

Abstract

Ensuring Quality of Service (QoS) and efficient load balancing in cloud communication networks is critical for optimizing resource allocation, minimizing latency, and enhancing service reliability. Traditional load balancing strategies often fail to scale and adapt to dynamic cloud environments, resulting in network congestion, resource underutilization, and increased operational costs. This study presents a novel Genetic Algorithm (GA)-based hyperheuristic optimization framework integrated with a mathematical model for QoS-aware load balancing, designed to address the challenges of scalability and efficiency. The model is referred to as GAHOQoS. We introduce valid inequalities to strengthen the optimization formulation, accelerating convergence and improving solution quality. Our GA-hyperheuristic framework dynamically selects and combines multiple low-level heuristics to optimize task allocation across cloud servers while adhering to QoS constraints such as latency, throughput, and energy efficiency. Experimental evaluations on a range of cloud communication scenarios demonstrate that GAHOQoS significantly reduces service latency, balances workload distribution, and optimizes resource utilization. Comparative analysis with existing metaheuristic methods, including GA−P SO and SA−GA, confirms that the proposed framework outperforms traditional approaches in terms of computational efficiency, scalability, and QoS satisfaction. GAHOQoS provides an adaptable, computationally efficient solution for enhancing cloud network performance, contributing to the development of high-performance, energy-efficient, and robust cloud infrastructures.