[Swift-commit] r3872 - text/parco10submission

[noreply at svn.ci.uchicago.edu]

Author: dsk
Date: 2011-01-06 15:26:48 -0600 (Thu, 06 Jan 2011)
New Revision: 3872

Modified:
   text/parco10submission/paper.tex
Log:
updates in related work


Modified: text/parco10submission/paper.tex
===================================================================
--- text/parco10submission/paper.tex	2011-01-06 19:42:30 UTC (rev 3871)
+++ text/parco10submission/paper.tex	2011-01-06 21:26:48 UTC (rev 3872)
@@ -1662,34 +1662,26 @@
 programming tool for the specification and execution of large parallel
 computations on large quantities of data, and facilitating the
 utilization of large distributed resources. However, the two also
-differ in many aspects:
+differ in many aspects.  The 
+MapReduce programming model supports key-value pairs as
+input or output datasets and two types of computation functions,
+map and reduce; Swift provides a type system and allows the
+definition of complex data structures and arbitrary computational
+procedures.
+In MapReduce, input and output data can be of
+several different formats, and it is also possible to define new
+data sources; Swift provides a more flexible mapping mechanism to
+map between logical data structures and various physical
+representations.
+Swift does not automatically partition input
+datasets as MapReduce does; Swift datasets can be organized in structures, and
+individual items in a dataset can be transferred accordingly along
+with computations.
+MapReduce schedules computations within a
+cluster with shared Google File System; Swift schedules across
+distributed Grid sites that may span multiple administrative
+domains, and deals with security and resource usage policy issues.
 
-\begin{itemize}
-
-\item Programming model: MapReduce only supports key-value pairs as
-  input or output datasets and two types of computation functions,
-  map and reduce; Swift provides a type system and allows the
-  definition of complex data structures and arbitrary computational
-  procedures.
-
-\item Data format: in MapReduce, input and output data can be of
-  several different formats, and it is also possible to define new
-  data sources. Swift provides a more flexible mapping mechanism to
-  map between logical data structures and various physical
-  representations.
-
-\item Dataset partition: Swift does not automatically partition input
-  datasets. Instead, datasets can be organized in structures, and
-  individual items in a dataset can be transferred accordingly along
-  with computations.
-
-\item Execution environment: MapReduce schedules computations within a
-  cluster with shared Google File System, where Swift schedules across
-  distributed Grid sites that may span multiple administrative
-  domains, and deals with security and resource usage policy issues.
-
-\end{itemize}
-
 BPEL~\cite{BPEL_2006} is a Web Service-based standard that specifies
 how a set of Web services interact to form a larger, composite Web
 Service. BPEL is starting to be tested in scientific contexts. While
@@ -1699,7 +1691,7 @@
 application with repetitive patterns on a collection of datasets could
 result in large, repetitive BPEL documents~\cite{Sedna_2007}, and BPEL
 is cumbersome if not impossible to write for computational
-scientists. Although BPEL can use \katznote{an?  the?} XML Schema to describe data types,
+scientists. Although BPEL can use an XML Schema to describe data types,
 it does not provide support for mapping between a logical XML view and
 arbitrary physical representations.