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From Apache Wiki <wikidi...@apache.org>
Subject [Incubator Wiki] Update of "MesosProposal" by MateiZaharia
Date Mon, 13 Dec 2010 21:17:24 GMT
Dear Wiki user,

You have subscribed to a wiki page or wiki category on "Incubator Wiki" for change notification.

The "MesosProposal" page has been changed by MateiZaharia.
http://wiki.apache.org/incubator/MesosProposal?action=diff&rev1=2&rev2=3

--------------------------------------------------

  = Proposal =
  
  Mesos is system for sharing resources between cluster applications such
- as Hadoop MapReduce, HBase, MPI, and web applications.
+ as Hadoop Map``Reduce, HBase, MPI, and web applications.
  It is motivated by three use cases. First, organizations that use
  several of these applications can use Mesos to share nodes between them,
  increasing utilization and simplifying management. Second, inspired by
- MapReduce, a wide array of new cluster programming frameworks are being
+ Map``Reduce, a wide array of new cluster programming frameworks are being
  proposed, such as Apache Hama, Microsoft Dryad, and Google's Pregel and
  Caffeine. Mesos provides a common interface for such frameworks to share
  resources, allowing organizations to use multiple frameworks in the same
@@ -28, +28 @@

  Mesos was inspired by operational issues experienced in large Apache Hadoop
  deployments as well as a desire to provide a management system for a
  wider range of cluster applications. The Apache Hadoop community has long
- realized that the current model of having one instance of MapReduce
+ realized that the current model of having one instance of Map``Reduce
  control a whole cluster leads to problems with isolation (one job may
  cause the master to crash, killing all the other jobs), scalability,
  and software upgrades (an upgrade must be deployed on the whole cluster).
- Statically partitioning resources into multiple fixed-size MapReduce clusters
+ Statically partitioning resources into multiple fixed-size Map``Reduce clusters
  is unattractive because it lowers both utilization and data locality.
  The community has discussed a two-level scheduling model where a simple,
  robust low-level layer enables multiple applications to launch tasks
@@ -56, +56 @@

  the cluster (e.g. X nodes for the full duration of the job).
  This is problematic because many cluster applications are elastic
  (can scale up and down), so utilization is not optimal under static
- partitioning, and because data-intensive applications such as MapReduce
+ partitioning, and because data-intensive applications such as Map``Reduce
  need to run a few tasks on every node of the cluster to read data locally.
  To address these challenges, Mesos is designed around two principles:
  

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