diff --git a/conf/spark-env.sh.template b/conf/spark-env.sh.template
index 0886b0276fb90..67f81d33361e1 100755
--- a/conf/spark-env.sh.template
+++ b/conf/spark-env.sh.template
@@ -15,7 +15,7 @@
 # - SPARK_PUBLIC_DNS, to set the public DNS name of the driver program
 # - SPARK_CLASSPATH, default classpath entries to append
 # - SPARK_LOCAL_DIRS, storage directories to use on this node for shuffle and RDD data
-# - MESOS_NATIVE_LIBRARY, to point to your libmesos.so if you use Mesos
+# - MESOS_NATIVE_JAVA_LIBRARY, to point to your libmesos.so if you use Mesos
 
 # Options read in YARN client mode
 # - HADOOP_CONF_DIR, to point Spark towards Hadoop configuration files
diff --git a/docs/running-on-mesos.md b/docs/running-on-mesos.md
index 78358499fd01f..b1db68f0292b8 100644
--- a/docs/running-on-mesos.md
+++ b/docs/running-on-mesos.md
@@ -110,7 +110,7 @@ cluster, or `mesos://zk://host:2181` for a multi-master Mesos cluster using ZooK
 The driver also needs some configuration in `spark-env.sh` to interact properly with Mesos:
 
 1. In `spark-env.sh` set some environment variables:
- * `export MESOS_NATIVE_LIBRARY=<path to libmesos.so>`. This path is typically
+ * `export MESOS_NATIVE_JAVA_LIBRARY=<path to libmesos.so>`. This path is typically
    `<prefix>/lib/libmesos.so` where the prefix is `/usr/local` by default. See Mesos installation
    instructions above. On Mac OS X, the library is called `libmesos.dylib` instead of
    `libmesos.so`.
@@ -167,9 +167,6 @@ acquire. By default, it will acquire *all* cores in the cluster (that get offere
 only makes sense if you run just one application at a time. You can cap the maximum number of cores
 using `conf.set("spark.cores.max", "10")` (for example).
 
-# Known issues
-- When using the "fine-grained" mode, make sure that your executors always leave 32 MB free on the slaves. Otherwise it can happen that your Spark job does not proceed anymore. Currently, Apache Mesos only offers resources if there are at least 32 MB memory allocatable. But as Spark allocates memory only for the executor and cpu only for tasks, it can happen on high slave memory usage that no new tasks will be started anymore. More details can be found in [MESOS-1688](https://issues.apache.org/jira/browse/MESOS-1688). Alternatively use the "coarse-gained" mode, which is not affected by this issue.
-
 # Running Alongside Hadoop
 
 You can run Spark and Mesos alongside your existing Hadoop cluster by just launching them as a
diff --git a/repl/scala-2.10/src/test/scala/org/apache/spark/repl/ReplSuite.scala b/repl/scala-2.10/src/test/scala/org/apache/spark/repl/ReplSuite.scala
index e594ad868ea1c..4f2c4dc588bd0 100644
--- a/repl/scala-2.10/src/test/scala/org/apache/spark/repl/ReplSuite.scala
+++ b/repl/scala-2.10/src/test/scala/org/apache/spark/repl/ReplSuite.scala
@@ -281,7 +281,7 @@ class ReplSuite extends FunSuite {
     assertDoesNotContain("Exception", output)
   }
 
-  if (System.getenv("MESOS_NATIVE_LIBRARY") != null) {
+  if (System.getenv("MESOS_NATIVE_JAVA_LIBRARY") != null) {
     test("running on Mesos") {
       val output = runInterpreter("localquiet",
         """
diff --git a/repl/scala-2.11/src/test/scala/org/apache/spark/repl/ReplSuite.scala b/repl/scala-2.11/src/test/scala/org/apache/spark/repl/ReplSuite.scala
index f966f25c5a14c..0086e857d1458 100644
--- a/repl/scala-2.11/src/test/scala/org/apache/spark/repl/ReplSuite.scala
+++ b/repl/scala-2.11/src/test/scala/org/apache/spark/repl/ReplSuite.scala
@@ -289,7 +289,7 @@ class ReplSuite extends FunSuite {
     assertDoesNotContain("Exception", output)
   }
 
-  if (System.getenv("MESOS_NATIVE_LIBRARY") != null) {
+  if (System.getenv("MESOS_NATIVE_JAVA_LIBRARY") != null) {
     test("running on Mesos") {
       val output = runInterpreter("localquiet",
         """