LAUSR

Cloud technology is a utility where different hardware and software resources are accessed on pay-per-user ground base. Most of these resources are available in virtualized form and virtual machine (VM) is one of the main elements of visualization. However, the tasks send by user to cloud may cause the VM to be under loaded or overloaded due to tasks allocation system in VM which lead to the failure of the system or delay the user tasks. Therefore, we propose an improved load balancing technique known as hybridizing artificial bee colony algorithm with Bat algorithm (HBAC). For searching food source employed bee’s use they share the information about to the food source to onlooker bee. In the initialization section equal number of employed bees and onlooker bees used for searching process with the same updation rule which make trapping in search process. Therefore for employed bee the Bat updation rule use in initialization section. When the employed bees share the information with onlooker bee with the help of dancing now it time for onlooker bee to prepare the candidate bee for searching process. Onlooker bees start searching for candidate bee using as technique in this technique it take cycle for searching bee if some tasks are missing in this cycle it take more cycle up to when all tasks are cover in the searching process. This technique take more time for that reason a new technique used in onlooker searching section which make the tasks are into equal part then start searching which was affective and take less time as compare to the previous one. The third modification took place at fitness value of artificial bee colony algorithm where the tasks distribution take more time due to overlapping which affect the tasks accuracy system. The proposed HBAC algorithm was tested and compared with other state-of-the-art algorithms on 200–2000 even tasks by using CloudSim on standard workload format (SWF) data sets file size (200 kb and 400 kb). The proposed HBAC showed an improved accuracy rate in tasks distribution and reduced the makespan of VM in a cloud data center. Based on the ANOVA comparison test results, a 1.25% improvement on accuracy and 0.98% reduced makespan on tasks allocation system of VM in cloud computing is observed with the proposed HBAC.