diff --git a/app-conf/FetcherConf.xml b/app-conf/FetcherConf.xml
index ebaa4bdde..1a6bb4a01 100644
--- a/app-conf/FetcherConf.xml
+++ b/app-conf/FetcherConf.xml
@@ -51,7 +51,7 @@
      To work properly, this fetcher should use the same timezone with the job history server.
      If not set, the local timezone will be used.
    -->
-  
+
   <fetcher>
     <applicationtype>mapreduce</applicationtype>
     <classname>com.linkedin.drelephant.mapreduce.fetchers.MapReduceFSFetcherHadoop2</classname>
@@ -61,7 +61,7 @@
       <history_server_time_zone>PST</history_server_time_zone>
     </params>
   </fetcher>
-  
+
 
   <!--
     FSFetcher for Spark. Loads the eventlog from HDFS and replays to get the metrics and application properties
diff --git a/app-conf/HeuristicConf.xml b/app-conf/HeuristicConf.xml
index 86cc65d40..b243fe5dc 100644
--- a/app-conf/HeuristicConf.xml
+++ b/app-conf/HeuristicConf.xml
@@ -193,6 +193,12 @@
     <classname>com.linkedin.drelephant.spark.heuristics.StagesHeuristic</classname>
     <viewname>views.html.help.spark.helpStagesHeuristic</viewname>
   </heuristic>
+  <heuristic>
+    <applicationtype>spark</applicationtype>
+    <heuristicname>JVM Used Memory</heuristicname>
+    <classname>com.linkedin.drelephant.spark.heuristics.JvmUsedMemoryHeuristic</classname>
+    <viewname>views.html.help.spark.helpJvmUsedMemoryHeuristic</viewname>
+  </heuristic>
   <heuristic>
     <applicationtype>spark</applicationtype>
     <heuristicname>Stages with failed tasks</heuristicname>
@@ -211,5 +217,5 @@
     <classname>com.linkedin.drelephant.spark.heuristics.ExecutorStorageSpillHeuristic</classname>
     <viewname>views.html.help.spark.helpExecutorStorageSpillHeuristic</viewname>
   </heuristic>
-  
+
 </heuristics>
diff --git a/app/com/linkedin/drelephant/spark/fetchers/statusapiv1/statusapiv1.scala b/app/com/linkedin/drelephant/spark/fetchers/statusapiv1/statusapiv1.scala
index ea3d29dbc..3f43a30b8 100644
--- a/app/com/linkedin/drelephant/spark/fetchers/statusapiv1/statusapiv1.scala
+++ b/app/com/linkedin/drelephant/spark/fetchers/statusapiv1/statusapiv1.scala
@@ -88,7 +88,9 @@ trait ExecutorSummary{
   def maxMemory: Long
   def totalGCTime: Long
   def totalMemoryBytesSpilled: Long
-  def executorLogs: Map[String, String]}
+  def executorLogs: Map[String, String]
+  def peakJvmUsedMemory: Map[String, Long]
+}
 
 trait JobData{
   def jobId: Int
@@ -295,7 +297,8 @@ class ExecutorSummaryImpl(
   var maxMemory: Long,
   var totalGCTime: Long,
   var totalMemoryBytesSpilled: Long,
-  var executorLogs: Map[String, String]) extends ExecutorSummary
+  var executorLogs: Map[String, String],
+  var peakJvmUsedMemory: Map[String, Long]) extends ExecutorSummary
 
 class JobDataImpl(
   var jobId: Int,
diff --git a/app/com/linkedin/drelephant/spark/heuristics/JvmUsedMemoryHeuristic.scala b/app/com/linkedin/drelephant/spark/heuristics/JvmUsedMemoryHeuristic.scala
new file mode 100644
index 000000000..f57aba948
--- /dev/null
+++ b/app/com/linkedin/drelephant/spark/heuristics/JvmUsedMemoryHeuristic.scala
@@ -0,0 +1,124 @@
+/*
+ * Copyright 2016 LinkedIn Corp.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License"); you may not
+ * use this file except in compliance with the License. You may obtain a copy of
+ * the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+ * License for the specific language governing permissions and limitations under
+ * the License.
+ */
+
+package com.linkedin.drelephant.spark.heuristics
+
+import com.linkedin.drelephant.analysis._
+import com.linkedin.drelephant.configurations.heuristic.HeuristicConfigurationData
+import com.linkedin.drelephant.spark.data.SparkApplicationData
+import com.linkedin.drelephant.spark.fetchers.statusapiv1.ExecutorSummary
+import com.linkedin.drelephant.util.MemoryFormatUtils
+
+import scala.collection.JavaConverters
+
+
+/**
+  * A heuristic based on peak JVM used memory for the spark executors and driver
+  *
+  */
+class JvmUsedMemoryHeuristic(private val heuristicConfigurationData: HeuristicConfigurationData)
+  extends Heuristic[SparkApplicationData] {
+
+  import JvmUsedMemoryHeuristic._
+  import JavaConverters._
+
+  override def getHeuristicConfData(): HeuristicConfigurationData = heuristicConfigurationData
+
+  lazy val executorPeakJvmMemoryThresholdString: String = heuristicConfigurationData.getParamMap.get(MAX_EXECUTOR_PEAK_JVM_USED_MEMORY_THRESHOLD_KEY)
+  lazy val driverPeakJvmMemoryThresholdString: String = heuristicConfigurationData.getParamMap.get(MAX_DRIVER_PEAK_JVM_USED_MEMORY_THRESHOLD_KEY)
+
+  override def apply(data: SparkApplicationData): HeuristicResult = {
+    val evaluator = new Evaluator(this, data)
+
+    var resultDetails = Seq(
+      new HeuristicResultDetails("Max executor peak JVM used memory", MemoryFormatUtils.bytesToString(evaluator.maxExecutorPeakJvmUsedMemory)),
+      new HeuristicResultDetails("Max driver peak JVM used memory", MemoryFormatUtils.bytesToString(evaluator.maxDriverPeakJvmUsedMemory)),
+      new HeuristicResultDetails("spark.executor.memory", MemoryFormatUtils.bytesToString(evaluator.sparkExecutorMemory)),
+      new HeuristicResultDetails("spark.driver.memory", MemoryFormatUtils.bytesToString(evaluator.sparkDriverMemory))
+    )
+
+    if(evaluator.severityExecutor.getValue > Severity.LOW.getValue) {
+      resultDetails :+ new HeuristicResultDetails("Executor Memory", "The allocated memory for the executor (in " + SPARK_EXECUTOR_MEMORY +") is much more than the peak JVM used memory by executors.")
+      resultDetails :+ new HeuristicResultDetails("Reasonable size for executor memory", ((1+BUFFER_PERCENT.toDouble/100.0)*evaluator.maxExecutorPeakJvmUsedMemory).toString)
+    }
+
+    if(evaluator.severityDriver.getValue > Severity.LOW.getValue) {
+      resultDetails :+ new HeuristicResultDetails("Driver Memory", "The allocated memory for the driver (in " + SPARK_DRIVER_MEMORY + ") is much more than the peak JVM used memory by the driver.")
+    }
+
+    val result = new HeuristicResult(
+      heuristicConfigurationData.getClassName,
+      heuristicConfigurationData.getHeuristicName,
+      evaluator.severity,
+      0,
+      resultDetails.asJava
+    )
+    result
+  }
+}
+
+object JvmUsedMemoryHeuristic {
+  val JVM_USED_MEMORY = "jvmUsedMemory"
+  val SPARK_EXECUTOR_MEMORY = "spark.executor.memory"
+  val SPARK_DRIVER_MEMORY = "spark.driver.memory"
+  // 300 * FileUtils.ONE_MB (300 * 1024 * 1024)
+  val reservedMemory : Long = 314572800
+  val BUFFER_PERCENT : Int = 20
+  val MAX_EXECUTOR_PEAK_JVM_USED_MEMORY_THRESHOLD_KEY = "executor_peak_jvm_memory_threshold"
+  val MAX_DRIVER_PEAK_JVM_USED_MEMORY_THRESHOLD_KEY = "driver_peak_jvm_memory_threshold"
+
+  class Evaluator(jvmUsedMemoryHeuristic: JvmUsedMemoryHeuristic, data: SparkApplicationData) {
+    lazy val appConfigurationProperties: Map[String, String] =
+      data.appConfigurationProperties
+
+    lazy val executorSummaries: Seq[ExecutorSummary] = data.executorSummaries
+    lazy val driverSummary : Option[ExecutorSummary] = executorSummaries.find(_.id.equals("driver"))
+    val maxDriverPeakJvmUsedMemory : Long = driverSummary.get.peakJvmUsedMemory.getOrElse(JVM_USED_MEMORY, 0L).asInstanceOf[Number].longValue
+    val executorList : Seq[ExecutorSummary] = executorSummaries.filterNot(_.id.equals("driver"))
+    val sparkExecutorMemory : Long = (appConfigurationProperties.get(SPARK_EXECUTOR_MEMORY).map(MemoryFormatUtils.stringToBytes)).getOrElse(0L)
+    val sparkDriverMemory : Long = appConfigurationProperties.get(SPARK_DRIVER_MEMORY).map(MemoryFormatUtils.stringToBytes).getOrElse(0L)
+    lazy val maxExecutorPeakJvmUsedMemory: Long = if (executorList.isEmpty) 0L else executorList.map {
+      _.peakJvmUsedMemory.getOrElse(JVM_USED_MEMORY, 0).asInstanceOf[Number].longValue
+    }.max
+
+    lazy val DEFAULT_MAX_EXECUTOR_PEAK_JVM_USED_MEMORY_THRESHOLDS =
+      SeverityThresholds(low = 1.5 * (maxExecutorPeakJvmUsedMemory + reservedMemory), moderate = 2 * (maxExecutorPeakJvmUsedMemory + reservedMemory), severe = 4 * (maxExecutorPeakJvmUsedMemory + reservedMemory), critical = 8 * (maxExecutorPeakJvmUsedMemory + reservedMemory), ascending = true)
+
+    lazy val DEFAULT_MAX_DRIVER_PEAK_JVM_USED_MEMORY_THRESHOLDS =
+      SeverityThresholds(low = 1.5 * (maxDriverPeakJvmUsedMemory + reservedMemory), moderate = 2 * (maxDriverPeakJvmUsedMemory + reservedMemory), severe = 4 * (maxDriverPeakJvmUsedMemory + reservedMemory), critical = 8 * (maxDriverPeakJvmUsedMemory + reservedMemory), ascending = true)
+
+    val MAX_EXECUTOR_PEAK_JVM_USED_MEMORY_THRESHOLDS : SeverityThresholds = if(jvmUsedMemoryHeuristic.executorPeakJvmMemoryThresholdString == null) {
+      DEFAULT_MAX_EXECUTOR_PEAK_JVM_USED_MEMORY_THRESHOLDS
+    } else {
+      SeverityThresholds.parse(jvmUsedMemoryHeuristic.executorPeakJvmMemoryThresholdString.split(",").map(_.toDouble * (maxExecutorPeakJvmUsedMemory + reservedMemory)).toString, ascending = false).getOrElse(DEFAULT_MAX_EXECUTOR_PEAK_JVM_USED_MEMORY_THRESHOLDS)
+    }
+
+    val MAX_DRIVER_PEAK_JVM_USED_MEMORY_THRESHOLDS : SeverityThresholds = if(jvmUsedMemoryHeuristic.driverPeakJvmMemoryThresholdString == null) {
+      DEFAULT_MAX_DRIVER_PEAK_JVM_USED_MEMORY_THRESHOLDS
+    } else {
+      SeverityThresholds.parse(jvmUsedMemoryHeuristic.driverPeakJvmMemoryThresholdString.split(",").map(_.toDouble * (maxDriverPeakJvmUsedMemory + reservedMemory)).toString, ascending = false).getOrElse(DEFAULT_MAX_DRIVER_PEAK_JVM_USED_MEMORY_THRESHOLDS)
+    }
+
+    val severityExecutor = MAX_EXECUTOR_PEAK_JVM_USED_MEMORY_THRESHOLDS.severityOf(sparkExecutorMemory)
+    val severityDriver = MAX_DRIVER_PEAK_JVM_USED_MEMORY_THRESHOLDS.severityOf(sparkDriverMemory)
+
+    /**
+      * disabling the skew check for executors
+      * val severitySkew = DEFAULT_JVM_MEMORY_SKEW_THRESHOLDS.severityOf(maxExecutorPeakJvmUsedMemory)
+      */
+    val severity : Severity = Severity.max(severityDriver, severityExecutor)
+  }
+}
diff --git a/app/com/linkedin/drelephant/spark/legacydata/LegacyDataConverters.scala b/app/com/linkedin/drelephant/spark/legacydata/LegacyDataConverters.scala
index 4909dbece..88344da5d 100644
--- a/app/com/linkedin/drelephant/spark/legacydata/LegacyDataConverters.scala
+++ b/app/com/linkedin/drelephant/spark/legacydata/LegacyDataConverters.scala
@@ -205,7 +205,8 @@ object LegacyDataConverters {
         executorInfo.maxMem,
         executorInfo.totalGCTime,
         executorInfo.totalMemoryBytesSpilled,
-        executorLogs = Map.empty
+        executorLogs = Map.empty,
+        peakJvmUsedMemory = Map.empty
       )
     }
 
diff --git a/app/views/help/spark/helpJvmUsedMemoryHeuristic.scala.html b/app/views/help/spark/helpJvmUsedMemoryHeuristic.scala.html
new file mode 100644
index 000000000..f58462821
--- /dev/null
+++ b/app/views/help/spark/helpJvmUsedMemoryHeuristic.scala.html
@@ -0,0 +1,20 @@
+@*
+* Copyright 2016 LinkedIn Corp.
+*
+* Licensed under the Apache License, Version 2.0 (the "License"); you may not
+* use this file except in compliance with the License. You may obtain a copy of
+* the License at
+*
+* http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+* License for the specific language governing permissions and limitations under
+* the License.
+*@
+<p>This is a heuristic for peak JVM used memory.</p>
+<h4>Executor Max Peak JVM Used Memory</h4>
+<p>This is to analyse whether the executor memory is set to a good value. To avoid wasted memory, it checks if the peak JVM used memory by the executor is reasonably close to the blocked executor memory which is specified in spark.executor.memory. If the peak JVM memory is much smaller, then the executor memory should be reduced.</p>
+<h4>Driver Max Peak JVM Used Memory</h4>
+<p>This is to analyse whether the driver memory is set to a good value. To avoid wasted memory, it checks if the peak JVM used memory by the driver is reasonably close to the blocked driver memory which is specified in spark.driver.memory. If the peak JVM memory is much smaller, then the driver memory should be reduced.</p>
diff --git a/test/com/linkedin/drelephant/spark/SparkMetricsAggregatorTest.scala b/test/com/linkedin/drelephant/spark/SparkMetricsAggregatorTest.scala
index fba3ea498..c979d5157 100644
--- a/test/com/linkedin/drelephant/spark/SparkMetricsAggregatorTest.scala
+++ b/test/com/linkedin/drelephant/spark/SparkMetricsAggregatorTest.scala
@@ -198,6 +198,7 @@ object SparkMetricsAggregatorTest {
     maxMemory = 0,
     totalGCTime = 0,
     totalMemoryBytesSpilled = 0,
-    executorLogs = Map.empty
+    executorLogs = Map.empty,
+    peakJvmUsedMemory = Map.empty
   )
 }
diff --git a/test/com/linkedin/drelephant/spark/heuristics/ExecutorGcHeuristicTest.scala b/test/com/linkedin/drelephant/spark/heuristics/ExecutorGcHeuristicTest.scala
index 13a4c54d1..765b08889 100644
--- a/test/com/linkedin/drelephant/spark/heuristics/ExecutorGcHeuristicTest.scala
+++ b/test/com/linkedin/drelephant/spark/heuristics/ExecutorGcHeuristicTest.scala
@@ -120,7 +120,8 @@ object ExecutorGcHeuristicTest {
     maxMemory = 0,
     totalGCTime,
     totalMemoryBytesSpilled = 0,
-    executorLogs = Map.empty
+    executorLogs = Map.empty,
+    peakJvmUsedMemory = Map.empty
   )
 
   def newFakeSparkApplicationData(
diff --git a/test/com/linkedin/drelephant/spark/heuristics/ExecutorsHeuristicTest.scala b/test/com/linkedin/drelephant/spark/heuristics/ExecutorsHeuristicTest.scala
index 64402e945..c83d9cbfb 100644
--- a/test/com/linkedin/drelephant/spark/heuristics/ExecutorsHeuristicTest.scala
+++ b/test/com/linkedin/drelephant/spark/heuristics/ExecutorsHeuristicTest.scala
@@ -251,7 +251,8 @@ object ExecutorsHeuristicTest {
     maxMemory,
     totalGCTime = 0,
     totalMemoryBytesSpilled = 0,
-    executorLogs = Map.empty
+    executorLogs = Map.empty,
+    peakJvmUsedMemory = Map.empty
   )
 
   def newFakeSparkApplicationData(executorSummaries: Seq[ExecutorSummaryImpl]): SparkApplicationData = {
diff --git a/test/com/linkedin/drelephant/spark/heuristics/JvmUsedMemoryHeuristicTest.scala b/test/com/linkedin/drelephant/spark/heuristics/JvmUsedMemoryHeuristicTest.scala
new file mode 100644
index 000000000..f138de427
--- /dev/null
+++ b/test/com/linkedin/drelephant/spark/heuristics/JvmUsedMemoryHeuristicTest.scala
@@ -0,0 +1,112 @@
+package com.linkedin.drelephant.spark.heuristics
+
+import com.linkedin.drelephant.analysis.{ApplicationType, Severity}
+import com.linkedin.drelephant.configurations.heuristic.HeuristicConfigurationData
+import com.linkedin.drelephant.spark.data.{SparkApplicationData, SparkLogDerivedData, SparkRestDerivedData}
+import com.linkedin.drelephant.spark.fetchers.statusapiv1.{ApplicationInfoImpl, ExecutorSummaryImpl}
+import org.apache.spark.scheduler.SparkListenerEnvironmentUpdate
+import org.scalatest.{FunSpec, Matchers}
+
+import scala.collection.JavaConverters
+
+class JvmUsedMemoryHeuristicTest extends FunSpec with Matchers {
+
+  import JvmUsedMemoryHeuristicTest._
+
+  val heuristicConfigurationData = newFakeHeuristicConfigurationData()
+
+  val peakJvmUsedMemoryHeuristic = new JvmUsedMemoryHeuristic(heuristicConfigurationData)
+
+  val appConfigurationProperties = Map("spark.driver.memory"->"40000000000", "spark.executor.memory"->"500000000")
+
+  val executorData = Seq(
+    newDummyExecutorData("1", Map("jvmUsedMemory" -> 394567123)),
+    newDummyExecutorData("2", Map("jvmUsedMemory" -> 23456834)),
+    newDummyExecutorData("3", Map("jvmUsedMemory" -> 334569)),
+    newDummyExecutorData("4", Map("jvmUsedMemory" -> 134563)),
+    newDummyExecutorData("5", Map("jvmUsedMemory" -> 234564)),
+    newDummyExecutorData("driver", Map("jvmUsedMemory" -> 394561))
+  )
+  describe(".apply") {
+    val data = newFakeSparkApplicationData(appConfigurationProperties, executorData)
+    val heuristicResult = peakJvmUsedMemoryHeuristic.apply(data)
+
+    it("has severity") {
+      heuristicResult.getSeverity should be(Severity.CRITICAL)
+    }
+
+    describe(".Evaluator") {
+      import JvmUsedMemoryHeuristic.Evaluator
+
+      val data = newFakeSparkApplicationData(appConfigurationProperties, executorData)
+      val evaluator = new Evaluator(peakJvmUsedMemoryHeuristic, data)
+
+      it("has severity executor") {
+        evaluator.severityExecutor should be(Severity.NONE)
+      }
+
+      it("has severity driver") {
+        evaluator.severityDriver should be(Severity.CRITICAL)
+      }
+
+      it("has max peak jvm memory") {
+        evaluator.maxExecutorPeakJvmUsedMemory should be (394567123)
+      }
+
+      it("has max driver peak jvm memory") {
+        evaluator.maxDriverPeakJvmUsedMemory should be (394561)
+      }
+    }
+  }
+}
+
+object JvmUsedMemoryHeuristicTest {
+
+  import JavaConverters._
+
+  def newFakeHeuristicConfigurationData(params: Map[String, String] = Map.empty): HeuristicConfigurationData =
+    new HeuristicConfigurationData("heuristic", "class", "view", new ApplicationType("type"), params.asJava)
+
+  def newDummyExecutorData(
+    id: String,
+    peakJvmUsedMemory: Map[String, Long]
+  ): ExecutorSummaryImpl = new ExecutorSummaryImpl(
+    id,
+    hostPort = "",
+    rddBlocks = 0,
+    memoryUsed = 0,
+    diskUsed = 0,
+    activeTasks = 0,
+    failedTasks = 0,
+    completedTasks = 0,
+    totalTasks = 0,
+    totalDuration = 0,
+    totalInputBytes = 0,
+    totalShuffleRead = 0,
+    totalShuffleWrite = 0,
+    maxMemory = 0,
+    totalGCTime = 0,
+    executorLogs = Map.empty,
+    peakJvmUsedMemory
+  )
+
+  def newFakeSparkApplicationData(
+    appConfigurationProperties: Map[String, String],
+    executorSummaries: Seq[ExecutorSummaryImpl]
+  ): SparkApplicationData = {
+
+    val logDerivedData = SparkLogDerivedData(
+      SparkListenerEnvironmentUpdate(Map("Spark Properties" -> appConfigurationProperties.toSeq))
+    )
+    val appId = "application_1"
+
+    val restDerivedData = SparkRestDerivedData(
+      new ApplicationInfoImpl(appId, name = "app", Seq.empty),
+      jobDatas = Seq.empty,
+      stageDatas = Seq.empty,
+      executorSummaries = executorSummaries
+    )
+
+    SparkApplicationData(appId, restDerivedData, Some(logDerivedData))
+  }
+}