A Cost-Efficient Resource Provisioning and Scheduling Approach for Deadline-Sensitive MapReduce Computations in Cloud Environment

Jabbari, Amir; Masoumiyan, Farzaneh; Hu, Shuwen; Tang, Maolin; Tian, Yu-Chu

doi:10.1109/cloud53861.2021.00078

Cited by 4 publications

(1 citation statement)

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“…Jabbari et al [43] addressed the challenge of selecting appropriate virtual machines (VMs) and distributing workload efficiently across them to meet both deadline and cost minimization goals in cloud environments. The paper proposes a cost minimization approach to calculate the total hiring cost before and during the computations, based on the application's input size and the required type and number of VMs.…”

Section: Deadline-aware Schedulers In Heterogeneous Clustersmentioning

confidence: 99%

MapReduce scheduling algorithms in Hadoop: a systematic study

Hedayati,

Maleki,

Olsson

et al. 2023

J Cloud Comp

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Hadoop is a framework for storing and processing huge volumes of data on clusters. It uses Hadoop Distributed File System (HDFS) for storing data and uses MapReduce to process that data. MapReduce is a parallel computing framework for processing large amounts of data on clusters. Scheduling is one of the most critical aspects of MapReduce. Scheduling in MapReduce is critical because it can have a significant impact on the performance and efficiency of the overall system. The goal of scheduling is to improve performance, minimize response times, and utilize resources efficiently. A systematic study of the existing scheduling algorithms is provided in this paper. Also, we provide a new classification of such schedulers and a review of each category. In addition, scheduling algorithms have been examined in terms of their main ideas, main objectives, advantages, and disadvantages.

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Section: Deadline-aware Schedulers In Heterogeneous Clustersmentioning

confidence: 99%