With the advent of the era of big data, many companies have taken the most important steps in the hybrid cloud to handle large amounts of data. In a hybrid cloud environment, cloud burst technology enables applications to be processed at a lower cost in a private cloud and burst into the public cloud when the resources of the private cloud are exhausted. However, there are many challenges in hybrid cloud environment, such as the heterogeneous jobs, different cloud providers and how to deploy a new application with minimum monetary cost. In this paper, the efficient job scheduling approach for heterogeneous workloads in private cloud is proposed to ensure high resource utilization. Moreover, the task scheduling method based on BP neural network in hybrid cloud is proposed to ensure that the tasks can be completed within the specified deadline of the user. The experimental results show that the efficient job scheduling approach can veffectively reduce the job response time and improve the throughput of cluster. The task scheduling method can reduce the response time of tasks, improve QoS satisfaction rate and minimize the cost of public cloud.

中文翻译：

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异构工作负载的混合云自适应调度策略

随着大数据时代的到来，许多公司已在混合云中采取了最重要的步骤来处理大量数据。在混合云环境中，云爆发技术使应用程序可以在私有云中以较低的成本进行处理，并在私有云的资源用尽时爆发到公共云中。但是，混合云环境面临许多挑战，例如异构作业，不同的云提供商以及如何以最低的金钱成本部署新应用程序。本文提出了一种针对私有云中异构工作负载的高效作业调度方法，以确保高资源利用率。此外，提出了一种基于BP神经网络的混合云任务调度方法，以确保任务能够在用户指定的期限内完成。实验结果表明，高效的作业调度方法可以有效地减少作业响应时间，提高集群的吞吐量。任务调度方法可以减少任务的响应时间，提高QoS满意率，并使公共云的成本最小化。