Optimal Resource Allocation in Cloud Computing Using Novel ACO-DE Algorithm

Cloud computing has emerged as a popular paradigm for delivering on-demand computing resources over the Internet. Resource allocation and load balancing are crucial elements of job scheduling because they enable the distribution of a growing volume of jobs to a finite number of virtual machines (VMs). The key objectives of load balancing and resource allocation are effective resource utilisation, performance optimisation, and cost optimisation. By achieving these objectives, cloud systems may provide effective services to meet user requirements and utilise resources as efficiently and economically as possible. In this study, we propose a novel approach for optimal resource allocation in cloud computing using a hybrid ant colony optimisation and differential evolution (ACO-DE) algorithm. In order to balance the load of VMs from cloud service providers (CSPs), the novel ACO-DE algorithm first generates the shortest routes, then allocates jobs to each VM to distribute resources in the most effective way. The exploration and exploitation abilities of ACO are used to produce an optimal solution, which is further refined using the DE operators. To evaluate the efficacy of the proposed algorithm, various experiments are carried out in a simulated environment to fulfil the key objective. The experiment results revealed the superior performance of the proposed algorithm over the ACO and DE algorithms individually.