objective particle swarm optimization

Cloud computing is a novel technology that provides users with better opportunities to gain access to services on the Internet. Users should utilize organizational services to meet their needs. They can also benefit from non-organizational services with high capacity but limited security. This study aims to provide a new security model that addresses security requirements for tasks and data as well as security strength for resources and communication paths. The proposed security model is defined security distance concept. Minimizing security distance has to do with task scheduling so that the resources can be matched with the security level and the data will be fitted into the appropriate communication path. The proposed scheduling algorithm takes the server profit into account in addition to the minimum security distance. The increased server profits can lead to higher resource sharing by the servers. The proposed scenario is implemented based on a neighborhood to search depth in task scheduling. This algorithm utilizes a novel ‘far and near neighborhood’ approach to select the best particle position. The approach generates both diversity and convergence in the set of answers. Finally, the proposed algorithm is compared with three other similar scheduling algorithms obtained by VNPSO, MPSO and NSGAII, considering the security of the cloud computing environment. The computational results show the effectiveness of the proposed algorithm to obtain resources with similar security and higher server profits.