Improving MapReduce Performance in Heterogeneous Environments Export

@article{citeulike:5796844, abstract = {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 can improve Hadoop response times by a factor of 2 in clusters of 200 virtual machines on EC2.}, author = {Zaharia, Matei and Konwinski, Andy and Joseph, Anthony D. and Katz, Randy and Stoica, Ion}, citeulike-article-id = {5796844}, citeulike-linkout-0 = {http://www.usenix.org/event/osdi08/tech/full_papers/zaharia/zaharia_html/}, journal = {USENIX Symposium on Operating Systems Design and Implementation (OSDI)}, keywords = {2008, cloud, distributed}, posted-at = {2009-09-30 14:13:04}, priority = {2}, title = {Improving MapReduce Performance in Heterogeneous Environments}, url = {http://www.usenix.org/event/osdi08/tech/full_papers/zaharia/zaharia_html/}, year = {2008} }

TY - JOUR ID - citeulike:5796844 L3 - citeulike-article-id:5796844 N2 - 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 can improve Hadoop response times by a factor of 2 in clusters of 200 virtual machines on EC2. JF - USENIX Symposium on Operating Systems Design and Implementation (OSDI) TI - Improving MapReduce Performance in Heterogeneous Environments KW - 2008 KW - cloud KW - distributed AU - Zaharia, Matei AU - Konwinski, Andy AU - Joseph, Anthony AU - Katz, Randy AU - Stoica, Ion PY - 2008/// UR - http://www.usenix.org/event/osdi08/tech/full_papers/zaharia/zaharia_html/ ER -