Multi-queue scheduling of heterogeneous jobs in hybrid geo-distributed cloud environment
Multi-queue scheduling of heterogeneous jobs in hybrid geo-distributed cloud environment
复制标题
混合地理分布式云环境下异构作业的多队列调度
DOI：
10.1007/s11227-018-2420-8
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发表时间：
2018-05
影响因子：
3.3
通讯作者：
Luo Youlong
中科院分区：
文献类型：
--
作者：
Li Chunlin;Tang Jianhang;Luo Youlong
In hybrid geo-distributed clouds, there is a technique named cloud bursting in which applications are handled in the private cloud with less expenses and burst into public clouds when the resources of the private cloud run out. However, how to deploy heterogeneous jobs in heterogeneous hybrid cloud environment is still a challenge. In this paper, a multi-queue scheduling approach of heterogeneous jobs for cloud bursting is proposed. In the private cloud, jobs are classified into I/O-intensive and CPU-intensive jobs, and nodes are divided into main I/O and CPU resource pools. Jobs are dispatched to corresponding resource pools to reduce the job execution time in heterogeneous cloud environment. A genetic algorithm is applied to schedule jobs to optimal job queues, which can reduce the job waiting time. Then, the execution time of each task is predicted by BP neural network. Jobs with high priority will be allocated to resources with the earliest finish time in the private cloud according to the prediction results. If the private cloud cannot meet the demand of users, public clouds with minimal costs will be applied. Experiments show that our proposed algorithm can reduce the average job response time and improve the throughput of the private cloud. It also can reduce the average task waiting time, average task execution time and average task response time significantly. Moreover, the costs of the hybrid clouds are reduced.
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DOI：
10.1016/j.future.2012.12.012
发表时间：
2013-06
期刊：
Future Gener. Comput. Syst.
影响因子：
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作者：
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DOI：
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DOI：
10.1109/cloud.2017.112
发表时间：
2017-06
期刊：
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影响因子：
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作者：
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Young Choon Lee;Bing Lian
影响因子：
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DOI：
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发表时间：
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影响因子：
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