Cloud computing is a popular platform for processing the tasks by utilizing Virtual Machines as executing elements. The problems such as utilization and makespan persist in task scheduling in cloud which has to be solved and hence this article presents a human‐inspired approach for solving the job shop scheduling issue in the cloud environment. Since the job shop scheduling is challenging under multicloud environment, this article improves the well‐known method which is termed as self‐adaptive Brain Storm Optimization scheme. As a result, the recommendation of solutions is improved and so the desired updating is done. With this context, the scheduling process is performed. Here, the allocation of jobs for resources of heterogeneous cloud is encoded as brain storming process. Furthermore, the resultant scheduling scheme is evaluated for different performance constraints such as resource utilization rate, job completion, and makes span and the outcomes are verified. Next, to the implementation, the proposed model is compared with BSO, Particle Swarm Optimization, Genetic Algorithm, and Differential Evolution and the analysis proves its better performance.

中文翻译：

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多云环境下作业车间调度的自适应头脑风暴

云计算是通过利用虚拟机作为执行元素来处理任务的流行平台。诸如利用率和完工时间等问题仍然存在于云中的任务调度中，必须解决，因此本文提出了一种解决云环境中作业车间调度问题的受人启发的方法。由于作业车间调度在多云环境下具有挑战性，本文改进了众所周知的方法，称为自适应脑风暴优化方案。结果，改进了解决方案的推荐，因此完成了所需的更新。在此上下文中，执行调度过程。在这里，异构云资源的作业分配被编码为头脑风暴过程。此外，针对不同的性能约束（例如资源利用率、作业完成情况和制造跨度）评估最终的调度方案，并验证结果。接下来，在实现方面，将所提出的模型与BSO、粒子群优化、遗传算法和差分进化进行了比较，分析证明了其更好的性能。