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Machine learning based secure and efficient task allocation in multi‐cloud
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AI
DOI：10.1002/cpe.7848.png)
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Abstract
En 中文
SummaryTask scheduling is a critical problem in the cloud computing environment since it has a significant impact on the system's performance. Security and robust based task allocation model improves the security of real‐time solicitations whereas preserving a high‐level performance of the data center. Here, to minimize the makespan in task allocation existing research proposed a bio‐inspired algorithm in a multi‐cloud environment, but, it needs more memory to update velocity and increases the processing time. Therefore, this research proposed a Machine Learning based Secure and Efficient Task Allocation in Multi‐Cloud Environment. First, the proposed model has a task classifier module that categorizes the matching it to the appropriate VM type for the task that was submitted. The next is the task allocation module, in which, an Intellectual Gravitational Search‐Genetic Algorithm is suggested to allocate the task to a VM. The tasks are distributed to the VMs in the order specified after the allocating phase in which they will be executed. Finally, to secure the task allocation in multi‐cloud, this research proposed a Naïve Bayes module to detect the attacks. The models and experimental findings demonstrate that our proposed approach produces a shorter makespan, lower cost, improved resource utilization, and better trade‐off between time and economic cost. It is more dependable and effective in multi‐cloud environment.
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