A load balancing model using bio inspired firefly algorithm in cloud computing - ., Vikas; Sai Vivek, M; Manohar, Pushyami (International Journal of Engineering and Technology, 2017)
Cloud computing is a model which helps in accessing the end-users interface which is adjustable as well as flexible service on Internet. The primary aim implemented scheme is for maximizing utilization of resource. It also offers a load which is good balanced compared to the all other resources in the servers of cloud. Some fundamental characteristics like usage of memory, process time, rate of the access, a resource load model can be deliver, by considering the load index which is new, present load computed to the virtual machine cloud server shared resources. If a load index calculated to the resources then initiation of balancing load for efficient utilization of resources. After all resources load index computed, the load balancing operation started to use the resources effectively by assigned resources processing for respective node for decreasing values of the load. The assignment of the resources to the nodes which are proper will become a problem i.e. optimal distribution. For this huge number of optimised schemes like algorithm of genetic, algorithm of modified genetic will be used to balance the load. But all these are may not be effective to offer best solutions. Because, the exploration issues are not overcome in these approaches. However, we can say that procedure of efficient optimisation is good for balancing load compared to others. Therefore, we implemented a new scheme for optimisation approach which is known as firefly algorithm for balancing the load. Initially the index table updated with the help of available virtual servers as well as request sequence. After that, based on new formulae, the load index will be calculated. The bal-ance of the load can be done with the help of load index using the algorithm of firefly. The results which are expected can be gathered and the implemented algorithm is efficient to balance the load in optimised time intervals.Â Â