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Greedy Scheduling for Distributed Computing Clusters

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To meet today's data management needs, it is a widespread practice to use distributed data storage and processing systems. Since the publication of the MapReduce paradigm, a plethora of such systems arose, but although widespread, the capabilities of these ...

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Hybrid, Job-Aware, and Preemptive Datacenter Scheduling

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Scheduling in datacenters is an important, yet challenging problem. Datacenters are composed of a large number, typically tens of thousands, of commodity computers running a variety of data-parallel jobs. The role of the scheduler is to assign cluster reso ...

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Don't cry over spilled records: Memory elasticity of data-parallel applications and its application to cluster scheduling

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Understanding the performance of data-parallel workloads when resource-constrained has significant practical importance but unfortunately has received only limited attention. This paper identifies, quantifies and demonstrates memory elasticity, an intrinsi ...

2017

Don't cry over spilled records: Memory elasticity of data-parallel applications and its application to cluster scheduling

Willy Zwaenepoel, Calin Iorgulescu, Syed Mohammad Aunn Raza, Florin Dinu, Wajih Ul Hassan

The USENIX Association2017

Job-aware Scheduling in Eagle: Divide and Stick to Your Probes

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We present Eagle, a new hybrid data center scheduler for data-parallel programs. Eagle dynamically divides the nodes of the data center in partitions for the execution of long and short jobs, thereby avoiding head-of-line blocking. Furthermore, it provides ...

ACM2016

Progressive algorithms are algorithms that, on the way to computing a complete solution to the problem at hand, output intermediate solutions that approximate the complete solution increasingly well. We present a framework for analyzing such algorithms, an ...

Carleton Univ, Dept Mathematics & Statistics2015