Dynamic resource allocation with otpimization techniques for qos in cloud computing

Abstract

Ensuring the quality of service (QoS) in cloud computing environments requires efficient resource allocation mechanisms to manage dynamic workloads and meet user demands. This paper proposes a dynamic resource allocation strategy that integrates gravitational search optimization (GSO) with Harris Hawks optimization (HHO) to optimize resource utilization and maintain QoS in cloud infrastructures. The proposed hybrid approach combines the global search capabilities of GSO, inspired by the law of gravity, with the exploitation and exploration strategies of HHO, mimicking the cooperative hunting behavior of Harris hawks. This synergy enables adaptive and efficient allocation of computational resources based on real-time workload fluctuations, reducing response times, minimizing energy consumption, and preventing Service Level Agreement (SLA) violations. By predicting workload variations and adjusting resource allocation dynamically, the proposed method ensures higher reliability, scalability, and cost-effectiveness compared to traditional resource allocation techniques. Simulation results demonstrate that the GSO-HHO-based approach outperforms conventional optimization algorithms in balancing the trade-offs between performance and resource efficiency, making it a robust solution for maintaining QoS in cloud computing environments.