LAUSR

Cloud computing is a powerful computing technology, which render a flexible services at anywhere to the user. One of the major issue of cloud computing was scheduling. In this work, a bacterial foraging optimization algorithm with genetic algorithm (GABFO) was combined to find out trustworthy scheduling problems in cloud workflow. Generally job scheduling and resource allocation in cloud is a tedious optimization problem at the time of considering QoS requirements. Lot of existing works under scheduling only concentrates on cost optimization and deadline problems, and it ignores the importance of reliability, availability and robustness. The main subscription of my work is to state a new optimized approach to schedule the jobs efficiently and allocate the resources in a efficient manner by introducing GABFO algorithm. Experiments were done in PSO, Genetic, BFO and then Genetic and BFO was combined to generate a hybrid optimized result, and the work was compared with above mentioned algorithms. The algorithms were executed for 52 iterations and totally 10 runs are calculated. The size of the job as well as virtual machines was varied for each iteration to calculate performance variation. We considered the optimization parameter as time and cost, and throughput. The work is implemented in cloudsim to create a simulated cloud environment. Final result shows better performance and maximum resource utilization in GABFO when compared to PSO, GA, BFO.