DAGWO based secure task scheduling in Multi-Cloud environment with risk probability

The Cloud with pay-per-use functioning has attained a great attraction towards on-demand applications. However, the availability of such services by a single data centre is limited, particularly, during the peak demand season. This is due to the fact that it has restricted resource availability. Therefore, the multi-cloud framework has been implemented. In this framework, more clouds get incorporated in a shared manner. All-private, public, or a blend of both may be a multi-cloud environment. The count of virtual machines is higher in the public cloud; however, security is not ensured. So far, most of the works have considered only the metrics like makespan, execution time, and execution time while allocating the tasks. However, the assurances of security while tasks’ execution is still an issue in many complex environments. This research work intends to propose a secure task scheduling scheme in the multi-cloud environment with the assessment of risk probability. The suggested study focuses on using the optimization idea to allocate the tasks in the best way possible. As a result, the suggested optimal task allocation includes four objectives like “makespan, execution time, utilization cost, and security constraints (risk evaluation)”. Also, a unique hybrid technique called Dragon Aided Grey Wolf optimization (DAGWO) is presented to address this optimization problem. Lastly, the performance of the suggested scheme is compared with theextantapproachesin terms of convergence, energy, makespan, etc. Especially, the risk probability of the proposed model while scheduling 100 tasks is 5.99%, 49.93%, 50.10%, 21.48%, and 31.557% better than existing PSO, WOA, DA, GWO, and MGWO methods respectively.