QoS-Aware Load Balancing in Cloud Computing Based on Chronological Kookaburra Optimization Algorithm

Abstract

In cloud computing environments, user requests often lead to varying load conditions across the system, resulting in underloaded, overloaded, or balanced states. Both underloading and overloading can cause system inefficiencies, including increased power consumption, prolonged execution times, and higher machine failure rates. Therefore, effective load balancing (LB) becomes a critical aspect of task scheduling in cloud systems, whether at the level of virtual machines (VMs) or independently. To address these challenges, this paper proposes the Chronological Kookaburra Optimization Algorithm (ChKOA) for efficient LB in cloud computing (CC). The proposed ChKOA is the combination of chronological concept with the Kookaburra Optimization Algorithm (KOA). Initially, tasks are assigned to VMs in a round-robin manner. Based on specific VM parameters, the VMs are classified into overloaded and underloaded categories using deep embedded clustering (DEC). Tasks in overloaded VMs are prioritized and redistributed to underloaded VMs, considering factors such as supply, demand, capacity, predicted load, and key Quality of Service (QoS) metrics, including resource availability and reliability. Load prediction is performed using a Deep Residual Network (DRN). Simulation results demonstrate that the proposed ChKOA achieves a balanced load of 0.535, capacity utilization of 0.954, resource availability of 0.954, reliability of 0.936, and a computational cost of 0.327 s.