LAUSR

Mobile cloud computing (MCC) is one of the evolving fields in recent years. The complexity of MCC made researchers to concentrate on efficient application development. In MCC, resource scheduling is treated as one of the major issues. Genetic Algorithms (GAs) are efficient search techniques to find the optimal solution for the scheduling problem. GAs has the ability to optimize the resource scheduling in both homogeneous and heterogeneous environments. This paper presents the multi objective genetic algorithm for MCC (MOGAMCC) environment. To implement the MOGAMCC, the cloudsim toolkit was extended with the MOGA and its task scheduling approach determines the optimal scheduling policy. A single point crossover model is employed for the generation of new population. Mutation process is carried by randomly changing the bit positions in the chromosomes. The experimental results show that the proposed model finds the optimal trade-off between the defined objectives and which ultimately reduces the makespan.