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Improving MapReduce Performance in Heterogeneous Environments

9 years 11 months ago
Improving MapReduce Performance in Heterogeneous Environments
MapReduce is emerging as an important programming model for large-scale data-parallel applications such as web indexing, data mining, and scientific simulation. Hadoop is an open-source implementation of MapReduce enjoying wide adoption and is often used for short jobs where low response time is critical. Hadoop's performance is closely tied to its task scheduler, which implicitly assumes that cluster nodes are homogeneous and tasks make progress linearly, and uses these assumptions to decide when to speculatively re-execute tasks that appear to be stragglers. In practice, the homogeneity assumptions do not always hold. An especially compelling setting where this occurs is a virtualized data center, such as Amazon's Elastic Compute Cloud (EC2). We show that Hadoop's scheduler can cause severe performance degradation in heterogeneous environments. We design a new scheduling algorithm, Longest Approximate Time to End (LATE), that is highly robust to heterogeneity. LATE ca...
Matei Zaharia, Andy Konwinski, Anthony D. Joseph,
Added 03 Dec 2009
Updated 03 Dec 2009
Type Conference
Year 2008
Where OSDI
Authors Matei Zaharia, Andy Konwinski, Anthony D. Joseph, Randy H. Katz, Ion Stoica
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