diff --git a/docs/running-on-yarn.md b/docs/running-on-yarn.md
index 0362f5a22331..9e054deace29 100644
--- a/docs/running-on-yarn.md
+++ b/docs/running-on-yarn.md
@@ -83,14 +83,14 @@ Most of the configs are the same for Spark on YARN as for other deployment modes
 </tr>
 <tr>
  <td><code>spark.yarn.executor.memoryOverhead</code></td>
-  <td>384</td>
+  <td>executorMemory * 0.06, with minimum of 384 </td>
   <td>
     The amount of off heap memory (in megabytes) to be allocated per executor. This is memory that accounts for things like VM overheads, interned strings, other native overheads, etc.
   </td>
 </tr>
 <tr>
   <td><code>spark.yarn.driver.memoryOverhead</code></td>
-  <td>384</td>
+  <td>driverMemory * 0.06, with minimum of 384 </td>
   <td>
     The amount of off heap memory (in megabytes) to be allocated per driver. This is memory that accounts for things like VM overheads, interned strings, other native overheads, etc.
   </td>
diff --git a/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/ExecutorLauncher.scala b/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/ExecutorLauncher.scala
index bfdb6232f511..d3d8e25621eb 100644
--- a/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/ExecutorLauncher.scala
+++ b/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/ExecutorLauncher.scala
@@ -100,7 +100,8 @@ class ExecutorLauncher(args: ApplicationMasterArguments, conf: Configuration, sp
 
     if (minimumMemory > 0) {
       val mem = args.executorMemory + sparkConf.getInt("spark.yarn.executor.memoryOverhead",
-        YarnAllocationHandler.MEMORY_OVERHEAD)
+        math.max((YarnAllocationHandler.MEMORY_OVERHEAD_FACTOR * args.executorMemory).toInt, 
+        YarnAllocationHandler.MEMORY_OVERHEAD_MIN))
       val numCore = (mem  / minimumMemory) + (if (0 != (mem % minimumMemory)) 1 else 0)
 
       if (numCore > 0) {
diff --git a/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocationHandler.scala b/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocationHandler.scala
index 80e0162e9f27..47d67b40875c 100644
--- a/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocationHandler.scala
+++ b/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocationHandler.scala
@@ -90,7 +90,8 @@ private[yarn] class YarnAllocationHandler(
 
   // Additional memory overhead - in mb.
   private def memoryOverhead: Int = sparkConf.getInt("spark.yarn.executor.memoryOverhead",
-    YarnAllocationHandler.MEMORY_OVERHEAD)
+    math.max((YarnAllocationHandler.MEMORY_OVERHEAD_FACTOR * executorMemory).toInt, 
+    YarnAllocationHandler.MEMORY_OVERHEAD_MIN))
 
   private val numExecutorsRunning = new AtomicInteger()
   // Used to generate a unique id per executor
@@ -548,8 +549,9 @@ object YarnAllocationHandler {
   // request types (like map/reduce in hadoop for example)
   val PRIORITY = 1
 
-  // Additional memory overhead - in mb
-  val MEMORY_OVERHEAD = 384
+  // Additional memory overhead
+  val MEMORY_OVERHEAD_FACTOR = 0.06
+  val MEMORY_OVERHEAD_MIN = 384
 
   // Host to rack map - saved from allocation requests
   // We are expecting this not to change.
diff --git a/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ClientBase.scala b/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ClientBase.scala
index 556f49342977..689db6299be6 100644
--- a/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ClientBase.scala
+++ b/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ClientBase.scala
@@ -67,7 +67,8 @@ trait ClientBase extends Logging {
 
   // Additional memory overhead - in mb.
   protected def memoryOverhead: Int = sparkConf.getInt("spark.yarn.driver.memoryOverhead",
-    YarnAllocationHandler.MEMORY_OVERHEAD)
+    math.max((YarnAllocationHandler.MEMORY_OVERHEAD_FACTOR * args.amMemory).toInt, 
+    YarnAllocationHandler.MEMORY_OVERHEAD_MIN))
 
   // TODO(harvey): This could just go in ClientArguments.
   def validateArgs() = {
diff --git a/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocationHandler.scala b/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocationHandler.scala
index 29ccec2adcac..c35ed62d77ee 100644
--- a/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocationHandler.scala
+++ b/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocationHandler.scala
@@ -92,7 +92,8 @@ private[yarn] class YarnAllocationHandler(
 
   // Additional memory overhead - in mb.
   private def memoryOverhead: Int = sparkConf.getInt("spark.yarn.executor.memoryOverhead",
-    YarnAllocationHandler.MEMORY_OVERHEAD)
+    math.max((YarnAllocationHandler.MEMORY_OVERHEAD_FACTOR * executorMemory).toInt, 
+    YarnAllocationHandler.MEMORY_OVERHEAD_MIN))
 
   // Number of container requests that have been sent to, but not yet allocated by the
   // ApplicationMaster.
@@ -562,8 +563,9 @@ object YarnAllocationHandler {
   // request types (like map/reduce in hadoop for example)
   val PRIORITY = 1
 
-  // Additional memory overhead - in mb.
-  val MEMORY_OVERHEAD = 384
+  // Additional memory overhead 
+  val MEMORY_OVERHEAD_FACTOR = 0.06
+  val MEMORY_OVERHEAD_MIN = 384
 
   // Host to rack map - saved from allocation requests. We are expecting this not to change.
   // Note that it is possible for this to change : and ResurceManager will indicate that to us via