1 / 23

Mesos: A Platform for Fine-Grained Resource Sharing in Datacenters

Background. In 2007, the RAD Lab began exploring data analytics to support developers, focusing on the fast-growing Hadoop platformSeveral projects looked at resource scheduling in MapReduce, leading to some code contributions to Hadoop

emma
Download Presentation

Mesos: A Platform for Fine-Grained Resource Sharing in Datacenters

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

    1. Mesos: A Platform for Fine-Grained Resource Sharing in Datacenters Benjamin Hindman, Andy Konwinski, Matei Zaharia, Ali Ghodsi, Anthony D. Joseph, Randy Katz, Scott Shenker, Ion Stoica

    2. Background In 2007, the RAD Lab began exploring data analytics to support developers, focusing on the fast-growing Hadoop platform Several projects looked at resource scheduling in MapReduce, leading to some code contributions to Hadoop & Dryad

    3. RAD Lab Work on MapReduce Scheduling LATE algorithm for straggler mitigation (M. Zaharia, A. Konwinski, A. Joseph, R. Katz, I. Stoica) Energy-efficient MapReduce (Y. Chen, L. Keys, A. Ganapathi, R. Katz) Hadoop Fair Scheduler and delay scheduling (M. Zaharia, S. Shenker, I. Stoica, and collaborators at Facebook and Yahoo! Research) Progress fairness (G. Lee and R. Katz) Mantri straggler mitigation (G. Ananthanarayanan, I. Stoica, and collaborators at Microsoft Research) Network scheduling (C. Reiss and R. Katz) Scarlett adaptive replication (G. Ananthanarayanan, S. Agarwal, I. Stoica, and collaborators at Microsoft)

    4. Motivation for Mesos Datacenters are running an increasingly diverse set of applications, both for analytics and user facing services Programming models: MapReduce, Dryad, Pregel, … Storage systems: HDFS, HBase, MySQL, … Web applications

More Related