Joint deadline-constrained and influence-aware design for allocating MapReduce jobs in cloud computing systems

Lin, Jenn-Wei; Arul, Joseph M.; Lin, Chi-Yi

doi:10.1007/s10586-018-1981-x

Cited by 5 publications

(2 citation statements)

References 10 publications

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“…Lin et al [45] provided a deadline-aware scheduler for MapReduce jobs, DGIA, in a heterogeneous environment. Using the data locality, DGIA meets the deadlines of new tasks.…”

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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“…Lin et al [45] provided a deadline-aware scheduler for MapReduce jobs, DGIA, in a heterogeneous environment. Using the data locality, DGIA meets the deadlines of new tasks.…”

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

View full text Add to dashboard Cite

show abstract

“…References 14,24 also discuss the dynamic deadline contain scheduling algorithm. The author in Reference 18 has also extended the multiobjective task scheduling as non‐dominated task scheduling, which may have multiple optimal solutions instead of single optimal solutions for scheduled tasks.…”

Section: Introductionmentioning

confidence: 99%

New efficient Hadoop scheduler: Generalized particle swarm optimization and simulated annealing‐dominant resource fairness

Sharma

Bharti²

2022

Concurrency and Computation

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Summary Digital growth during the Corona pandemic has generated massive data. The Hadoop in big data has to be more efficient in resource handling and job scheduling. This article proposes the improved job scheduler which is more efficient in fair job scheduling even with the heterogeneous resources. The faster job execution depends upon the localization too. The nearer the slots are, the faster is the execution. So, this article proposes a hybrid metaheuristic algorithm with fair scheduling and data locality as the two objectives in job scheduling. The dominant resource fairness policy in Hadoop YARN is updated by hybrid generalized particle swarm optimization and simulated annealing for minimum locality and maximum fairness in scheduling. The algorithm is tested on various workloads for heterogeneous resources.

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Two phased Yarn scheduling with novel binary hypergraphed PSO

Bharti²

2023

J Supercomput

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Joint deadline-constrained and influence-aware design for allocating MapReduce jobs in cloud computing systems

Cited by 5 publications

References 10 publications

MapReduce scheduling algorithms in Hadoop: a systematic study

MapReduce scheduling algorithms in Hadoop: a systematic study

New efficient Hadoop scheduler: Generalized particle swarm optimization and simulated annealing‐dominant resource fairness

Two phased Yarn scheduling with novel binary hypergraphed PSO

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