An efficient load balancing technique based on cuckoo search and firefly algorithm in cloud

Abstract

In recent years, cloud Load balancing (CLB) is the significant research area because vast data are stored on the servers leads to increase the loads on cloud servers. A trade-off on servers are maintained by LB techniques by distributing less power with equal load. In this paper, Cuckoo Search with Firefly Algorithm (CS-FA) is proposed for LB in a cloud environment. Initially, the capacity and load of each virtual machine are calculated. If the load of the virtual machine is greater than the balanced threshold value then, the LB algorithm is used for allocating the tasks. The CS-FA algorithm selects the best Virtual Machines (VMs) for assigning the tasks and migrate the overloaded VMs tasks to under-loaded VMs task. This algorithm majorly avoids the imbalanced workload performances in a cloud environment. The performance of proposed CS-FA method is compared with existing LB techniques such as Dynamic LB (DLB), Hybrid Dynamic LB (HDLB) and Honey Bee Behavior LB (HBB-LB) to evaluate the capacity and load of methods. The results showed that the existing HDLB method migrate seven tasks whereas the CS-FA method migrates only two tasks. The experimental result shows that proposed CS-FA method migrate only two tasks when number of loads is 40 and existing method migrate 6 tasks.