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Two-Stage Job Scheduling Model Based on Revenues and Resources

Abstract : In the big data platform, multiple users share the resources of the platform. For platform providers, it is a problem to be solved urgently that how to multi-user jobs are scheduled efficiently to take full advantage of the resources of the platform, get the maximum revenue and meet the SLA requirements of the users. We research the project of job scheduling for MapReduce framework further. The paper proposes a two-stage job scheduling model based on revenues and resources. In the model, we design a scheduling algorithm of the maximum revenue (SMR) based on the latest start time of the jobs. The SMR algorithm ensures that the jobs which have larger revenues can be completed before the deadlines of the jobs, and then providers can gain the largest total revenue. Under the premise of ensuring the maximum revenue, a sequence adjustment scheduling algorithm based on the maximum resource utilization of the platform (SAS) is developed to improve the resource utilization of the platform. Experimental results show that the two-stage job scheduling model proposed in this paper not only realizes the maximum revenue of the provider, but also improves the resource utilization of the platform and the comprehensive performance of the platform. What is more, the model has great practicability and reliability.
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Yuliang Shi, Dong Liu, Jing Hu, Jianlin Zhang. Two-Stage Job Scheduling Model Based on Revenues and Resources. 14th IFIP International Conference on Network and Parallel Computing (NPC), Oct 2017, Hefei, China. pp.37-48, ⟨10.1007/978-3-319-68210-5_4⟩. ⟨hal-01705450⟩

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