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The Journal of Supercomputing

Erschienen in:

01.08.2014

A novel real-time scheduling algorithm and performance analysis of a MapReduce-based cloud

verfasst von: Fei Teng, Frédéric Magoulès, Lei Yu, Tianrui Li

Erschienen in: The Journal of Supercomputing | Ausgabe 2/2014

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Abstract

MapReduce, a popular programming model for processing data-intensive tasks, has achieved great success in a wide range of applications such as search indexing, social network mining, collaborative recommendation, and spam detection. However, the ability of MapReduce is limited in two respects by its default schedulers. First, it does not support concurrent services sharing a cloud datacenter and second, it fails to guarantee response time for deadline-constrained services. This paper proposes the Paused Rate Monotonic (PRM) algorithm for scheduling hard real-time tasks on a MapReduce-based cloud. The scheduling performance is analyzed theoretically. We prove a bound on cluster utilization, which can be used as a sufficient condition to test whether a given task set can be scheduled. Both the theoretical analysis and experimental evaluation show that the PRM algorithm outperforms traditional real-time ones by improving the probability that a real-time task set can be scheduled on a MapReduce-based cloud.

Vorheriger Artikel An augmented Lagrangian method for VLSI global placement

Nächster Artikel A shortly connected mesh topology for high performance and energy efficient network-on-chip architectures

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Titel: A novel real-time scheduling algorithm and performance analysis of a MapReduce-based cloud
verfasst von: Fei Teng
Frédéric Magoulès
Lei Yu
Tianrui Li
Publikationsdatum: 01.08.2014
Verlag: Springer US
Erschienen in: The Journal of Supercomputing / Ausgabe 2/2014
Print ISSN: 0920-8542
Elektronische ISSN: 1573-0484
DOI: https://doi.org/10.1007/s11227-014-1115-z

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