a metaheuristic for dynamic load balancing in cloud environments

In the past years, cloud computing has become a popular paradigm that provides computing over the internet. with the internet facility, resources are provided dynamically to the end users in an on-demand fashion. users could be billions in number accessing the cloud. their need for services have been increasing at an alarming rate. to enhance the performance of the system, resources should be used efficiently. cloud environments have been defined in order to provide the effective services and have different advantages but they encounter some challenges such as load balancing. load balancing is one of the main issues in cloud computing, in which workload is distributed dynamically to all the nodes. also, with the rapid development and wide application of cloud computing, how to reduce the energy consumption of cloud data center has become a major issue of green cloud data center. the present study provides a metaheuristic firefly algorithm (fa), which aims to shorten the distance with optimums through multiple iterations and reduce the cost on the process of task allocated to achieve the efficient search for the optimal virtual machines. the results show that fa achieves the high-speed search for the virtual machines which are more eligible to perform tasks and save energy consumption of cloud data center relatively. the obtained results show better performance for fa in comparison with particle swarm optimization algorithm (pso).