diff --git a/conf/spark-env.sh.template b/conf/spark-env.sh.template
index 0886b0276fb90d24c0957abeffbc9aee3ddafab4..67f81d33361e1623b05d591be6d71d0304e4734c 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 e509e4bf373968daaa9564c099c884d4143bd32b..59a3e9d25baf10e76ec114b691200c92c86437b0 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 529914a2b6141f19b844527235cb6be6dc6eba37..249f438459300df4fdac94e0d2afb5f0330b4540 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 ed9b207a86a0bb767d2ce2c581e7b610cccd487d..b3bd1355481243e50a594777fd32b7fddd7c3aac 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",
         """