MCFSGO: an energy-efficient multi-adaptive firebug swarm genetic optimisation algorithm for dynamic resource scheduling in cloud environments

Towards computing infrastructure in the cloud, resource management becomes challenging. In general, energy consumption (CE) in the cloud environment resulted in high operational costs. Regarding issues with resource distribution, the virtual machine (VM) configuration tends to degrade the quality of service (QoS); having an overloaded host directly affects service level agreements (SLAs) and resource utilisation. To solve the problem, this paper proposed a bio-inspired firebug swarm optimisation (FSO) algorithm to assign VMs in cloud effectively. The proposed algorithm introduces a threshold optimum (TO) algorithm for VMs in the data-centre in order to differentiate with other algorithms: ant colony optimisation (ACO), binary gravity search algorithm (BSGA), particle swarm optimisation (PSO) and emperor penguin optimisation (EPO). The outcome of this proposed algorithm was experimented using the CloudSim simulation platform. Specifically, it achieves up to a 15% reduction in energy consumption, maintains SLA violations below 10%, and reduces the migration time by over 20%.