This article proposes the MapReduce scheduler with deadline and priorities (MRS-DP) scheduler capable of handling jobs with deadlines and priorities. Big data have emerged as a key concept and revolutionized data analytics in the present era. Big data are characterized by multiple dimensions or Vs, namely volume, velocity, variety, veracity, and valence. Recently, a new and important dimension (another V) is added, known as value. Value has emerged as an important characteristic and it can be understood in terms of delay in acquiring information, leading to late decisions that may result in missed opportunities. To gain optimal benefits, this article introduces a scheduler based on jobs with deadlines and priorities intending to improve resource utilization, with efficient job progress monitoring and backup launching mechanism. The proposed scheduler is capable of accommodating multiple jobs to maximize the number of jobs processed successfully and avoid starvation of lower priority jobs while improving the resource utilization and ensuring the assured quality of service (QoS). To evaluate our proposed scheduler, we ran multiple workloads consisting of the WordCount jobs and DataSort jobs. The performance of the proposed MRS-DP scheduler is compared with minimal earliest deadline first-work conserving scheduler and MapReduce Constraint Programming based Resource Management algorithm in terms of the percentage of successful jobs, priority-wise jobs, and resource utilization of the cluster. The result of the proposed scheduler depicts an improvement of around 10%–20% in terms of the percentage of successful jobs, 20%–25% concerning effective resource utilization offered, and the ability to ensure the offered QoS.

中文翻译：

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MRS-DP：提高具有截止日期和优先级的大数据应用程序的性能和资源利用率。

本文提出了具有期限和优先级的 MapReduce 调度器（MRS-DP）调度器，能够处理具有期限和优先级的作业。大数据已成为当今时代的一个关键概念，并彻底改变了数据分析。大数据具有多个维度或 V 的特征，即容量、速度、多样性、真实性和效价。最近，添加了一个新的重要维度（另一个 V），称为值。价值已经成为一个重要的特征，它可以理解为获取信息的延迟，从而导致可能导致错失机会的延迟决策。为了获得最佳收益，本文介绍了一种基于具有截止日期和优先级的作业的调度程序，旨在提高资源利用率，具有高效的作业进度监控和备份启动机制。提议的调度器能够容纳多个作业，以最大限度地增加成功处理的作业数量，避免低优先级作业的饥饿，同时提高资源利用率并确保有保证的服务质量 (QoS)。为了评估我们提议的调度程序，我们运行了多个工作负载，包括 WordCount 作业和 DataSort 作业。在成功作业的百分比、优先级作业和集群的资源利用率方面，将所提出的 MRS-DP 调度器的性能与最小最早截止日期第一工作保存调度器和基于 MapReduce 约束编程的资源管理算法进行了比较。所提议的调度程序的结果显示，成功作业的百分比提高了约 10%–20%，提供的有效资源利用率提高了 20%–25%，