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Abstract

In cloud environments, task scheduling is essential for improving performance. Nevertheless, the existence of several heterogeneous clouds makes scheduling extremely difficult, requiring increasingly advanced algorithms to manage these environments' diversity and dynamic nature. To solve this, numerous authors have created a variety of task schedulers utilizing heuristic and metaheuristic techniques. Nevertheless, it remains dynamic and challenging because task scheduling is an NP-hard issue. Furthermore, in many complicated situations, it is still problematic to guarantee security throughout the task’s execution. Therefore, this paper introduces a multi-objective security-aware task scheduler using the Crayfish Mud Ring Optimization Algorithm for a multi-cloud environment. This approach combines the Mud Ring Optimization approach and the Crayfish Optimization Algorithm to improve the results and overcome their shortcomings. Due to this hybridization, the proposed algorithm can avoid local optima and converge toward globally optimal solutions. Thus, the suggested scheduling algorithm offers the best work allocation, considering four objectives: makespan, cost energy, and security limitations (risk assessment). Comprehensive simulations are run on two real-world workloads, such as High-Performance Computing Center North and NASA Ames iPSC/860, and outperforming all comparison algorithms with an average improvement of 46% makespan, 55% energy, 61% cost, and 52% security risk in all the examined scenarios. The outcomes show that the suggested CMROA performs better and is suitable for a multi-cloud environment.

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

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