Dynamic Resource Allocation in Cloud Computing Environments Using Hybrid Swarm Intelligence Algorithms

The delivery of computational resources has been made convenient by the use of cloud computing due to ondemand provisioning that is self-service in nature and balances the user demand. Resource management in cloud environment nevertheless poses a rigorous task owing to dynamic workload, resource variability and performance optimization. This research focuses on the use of metaheuristic techniques as a combination of two or more swarm intelligence algorithm in improving the resource allocation in cloud computing domain. Proposing a hybrid solution of Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO), the allocation work is internally optimized to minimize process lags, energy expenditure and use of resources efficiently. PSO contributes the method's global search capability while ACO offers the local search exploitation necessary to adapt resources in real time. Numerous evaluations in miscellaneous and distributed cloud scenarios reveal that the proposed hybrid model is superior to conventional algorithms in durations of execution, costs, and load management. However, the research also looks at the scalability and robustness when operating the system under different levels of load and under single and multiple faults. The studies show that utilizing the hybrid SI as a feasible solution for solving resource allocation issues in cloud computing should be possible. The findings of this study help enhance intelligent energy-efficient adaptive cloud systems consequently advancing resource management for the future computing environment.