2015 IEEE International Conference on Service Operations and Logistics, and Informatics (SOLI) 2015
DOI: 10.1109/soli.2015.7367619
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MR-SPS: Scalable parallel scheduler for YARN/MapReduce platform

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Cited by 2 publications
(3 citation statements)
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“…Resource assignment in their work is to assign one or multiple nodes to the application, which is different from task assignment in cluster computing. MR-SPS [21] designs a scalable parallel scheduling algorithm which improves scalability and performance of a cluster by managing workload and data locality. Studies [22]- [24] further investigate storage-related resource management problems, in order to improve the system performance bottlenecked by I/Os.…”
Section: Related Workmentioning
confidence: 99%
“…Resource assignment in their work is to assign one or multiple nodes to the application, which is different from task assignment in cluster computing. MR-SPS [21] designs a scalable parallel scheduling algorithm which improves scalability and performance of a cluster by managing workload and data locality. Studies [22]- [24] further investigate storage-related resource management problems, in order to improve the system performance bottlenecked by I/Os.…”
Section: Related Workmentioning
confidence: 99%
“…The Scheduler component of the Yarn ResourceManager allocates resources to execute applications. This is an example of renovate cluster utilization in terms of CPU cores, fairness, memory etc [17].…”
Section: Resource Managermentioning
confidence: 99%
“…It is answerable only for allocation of resources to executing applications. The scheduling in [17] Yarn is a pluggable framework to allocate cluster resources in a lot of the user environment. Be conditional on the use case and user requirement, administrators may prefer either a simple FIFO scheduler, capacity scheduler, or fair scheduler [16].…”
Section: The Hadoop Yarn Scheduler Componentsmentioning
confidence: 99%