Abstract: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.

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SummaryThere is a good opportunity for enlightening the services of the mobile devices by introducing computational offloading using cloud technology. Offloading is a process for managing the complexity of the mobile environment by migrating computational load to the cloud. The mobile devices oblige the quick response for the offloading requests; it is dependent on network connectivity. The cloud services take long set‐up time irrespective of network connectivity. In this paper, new system architecture for the dynamic task offloading in the mobile cloud environment is proposed. The architecture includes the offloading algorithm that concentrates on energy consumption of the tasks both in the local and remote environment. The proposed algorithm formulates a collective task execution model for minimizing the energy consumption. The architecture concentrates on the network model by considering the task completion time in three different network scenarios. The experimental results show the efficiency of the suggested architecture in reducing the energy consumption and completion time of the tasks.

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