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 MapRe-
duce enjoying wide adoption and is often used for short
jobs where low response time is critical. Hadoop’s per-
formance is closely tied to its task scheduler, which im-
plicitly assumes that cluster nodes are homogeneous and
tasks make progress linearly, and uses these assumptions
to decide when to speculatively re-execute tasks that ap-
pear to be stragglers. In practice, the homogeneity as-
sumptions 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 can improve Hadoop response times by a factor
of 2 in clusters of 200 virtual machines on EC2.