[7.14] [ML] prevent accidentally asking for more resources when scali…

…ng down and improve scaling size estimations (#74691) (#74781)

* [ML] prevent accidentally asking for more resources when scaling down and improve scaling size estimations (#74691)

This commit addresses two problems:

 - Our memory estimations are not very exact. Consequently, its possible to request for too much or too little by a handful of KBs, while this is not a large issue in ESS, for custom tier sizes, it may be.
 - When scaling down, it was possible that part of the scale down was actually a scale up! This was due to some floating point rounding errors and poor estimations. Even though are estimations are better, it is best to NOT request higher resources in a scale down, no matter what.

One of the ways we improve the calculation is during JVM size calculations. Instead of having the knot point be `2gb` it has been changed to `1.2gb`. This accounts for the "window of uncertainty" for JVM sizes.

closes: #74709

x-pack/plugin/ml/qa/native-multi-node-tests/src/javaRestTest/java/org/elasticsearch/xpack/ml/integration/AutoscalingIT.java

@@ -26,6 +26,7 @@
 import org.elasticsearch.xpack.ml.autoscaling.MlAutoscalingDeciderService;
 import org.elasticsearch.xpack.ml.autoscaling.NativeMemoryCapacity;
 
+import java.util.Arrays;
 import java.util.Collections;
 import java.util.List;
 import java.util.SortedMap;
@@ -35,7 +36,7 @@
 
 import static org.elasticsearch.test.hamcrest.ElasticsearchAssertions.assertAcked;
 import static org.hamcrest.Matchers.containsString;
-import static org.hamcrest.Matchers.equalTo;
+import static org.hamcrest.Matchers.greaterThanOrEqualTo;
 import static org.hamcrest.Matchers.hasKey;
 
 public class AutoscalingIT extends MlNativeAutodetectIntegTestCase {
@@ -46,25 +47,37 @@ public class AutoscalingIT extends MlNativeAutodetectIntegTestCase {
 
     // This test assumes that xpack.ml.max_machine_memory_percent is 30
     // and that xpack.ml.use_auto_machine_memory_percent is false
-    public void testMLAutoscalingCapacity() {
+    public void testMLAutoscalingCapacity() throws Exception {
         SortedMap<String, Settings> deciders = new TreeMap<>();
         deciders.put(MlAutoscalingDeciderService.NAME,
             Settings.builder().put(MlAutoscalingDeciderService.DOWN_SCALE_DELAY.getKey(), TimeValue.ZERO).build());
         final PutAutoscalingPolicyAction.Request request = new PutAutoscalingPolicyAction.Request(
             "ml_test",
-            new TreeSet<>(Set.of("ml")),
+            new TreeSet<>(Arrays.asList(
+                "transform",
+                "data_frozen",
+                "master",
+                "remote_cluster_client",
+                "data",
+                "ml",
+                "data_content",
+                "data_hot",
+                "data_warm",
+                "data_cold",
+                "ingest"
+            )),
             deciders
         );
         assertAcked(client().execute(PutAutoscalingPolicyAction.INSTANCE, request).actionGet());
 
-        assertMlCapacity(
+        assertBusy(() -> assertMlCapacity(
             client().execute(
                 GetAutoscalingCapacityAction.INSTANCE,
                 new GetAutoscalingCapacityAction.Request()
             ).actionGet(),
             "Requesting scale down as tier and/or node size could be smaller",
             0L,
-            0L);
+            0L));
 
         putJob("job1", 100);
         putJob("job2", 200);
@@ -151,8 +164,8 @@ private void assertMlCapacity(GetAutoscalingCapacityAction.Response capacity, St
 
         AutoscalingDeciderResult autoscalingDeciderResult = autoscalingDeciderResults.results().get("ml");
         assertThat(autoscalingDeciderResult.reason().summary(), containsString(reason));
-        assertThat(autoscalingDeciderResult.requiredCapacity().total().memory().getBytes(), equalTo(tierBytes));
-        assertThat(autoscalingDeciderResult.requiredCapacity().node().memory().getBytes(), equalTo(nodeBytes));
+        assertThat(autoscalingDeciderResult.requiredCapacity().total().memory().getBytes(), greaterThanOrEqualTo(tierBytes - 1L));
+        assertThat(autoscalingDeciderResult.requiredCapacity().node().memory().getBytes(), greaterThanOrEqualTo(nodeBytes - 1L));
     }
 
     private void putJob(String jobId, long limitMb) {

x-pack/plugin/ml/src/main/java/org/elasticsearch/xpack/ml/autoscaling/MlAutoscalingDeciderService.java

@@ -71,6 +71,8 @@ public class MlAutoscalingDeciderService implements AutoscalingDeciderService,
     private static final Duration DEFAULT_MEMORY_REFRESH_RATE = Duration.ofMinutes(15);
     private static final String MEMORY_STALE = "unable to make scaling decision as job memory requirements are stale";
     private static final long NO_SCALE_DOWN_POSSIBLE = -1L;
+    // If ensureScaleDown changes the calculation by more than this much, log the error
+    private static final long ACCEPTABLE_DIFFERENCE = ByteSizeValue.ofMb(1).getBytes();
 
     public static final String NAME = "ml";
     public static final Setting<Integer> NUM_ANOMALY_JOBS_IN_QUEUE = Setting.intSetting("num_anomaly_jobs_in_queue", 0, 0);
@@ -359,6 +361,7 @@ public AutoscalingDeciderResult scale(Settings configuration, AutoscalingDecider
 
         final List<DiscoveryNode> nodes = getNodes(clusterState);
         final NativeMemoryCapacity currentScale = currentScale(nodes);
+
         final MlScalingReason.Builder reasonBuilder = MlScalingReason.builder()
             .setWaitingAnomalyJobs(waitingAnomalyJobs)
             .setWaitingAnalyticsJobs(waitingAnalyticsJobs)
@@ -527,9 +530,20 @@ public AutoscalingDeciderResult scale(Settings configuration, AutoscalingDecider
                     .build()));
         }
 
-        final Optional<AutoscalingDeciderResult> scaleDownDecision = checkForScaleDown(nodeLoads, largestJob, currentScale, reasonBuilder);
+        final Optional<AutoscalingDeciderResult> maybeScaleDown = checkForScaleDown(nodeLoads, largestJob, currentScale, reasonBuilder)
+            // Due to weird rounding errors, it may be that a scale down result COULD cause a scale up
+            // Ensuring the scaleDown here forces the scale down result to always be lower than the current capacity.
+            // This is safe as we know that ALL jobs are assigned at the current capacity
+            .map(result -> {
+                AutoscalingCapacity capacity = ensureScaleDown(result.requiredCapacity(), context.currentCapacity());
+                if (capacity == null) {
+                    return null;
+                }
+                return new AutoscalingDeciderResult(capacity, result.reason());
+            });
 
-        if (scaleDownDecision.isPresent()) {
+        if (maybeScaleDown.isPresent()) {
+            final AutoscalingDeciderResult scaleDownDecisionResult = maybeScaleDown.get();
             // Given maxOpenJobs, could we scale down to just one node?
             // We have no way of saying "we need X nodes"
             if (nodeLoads.size() > 1) {
@@ -546,22 +560,22 @@ public AutoscalingDeciderResult scale(Settings configuration, AutoscalingDecider
                         MAX_OPEN_JOBS_PER_NODE.getKey());
                     logger.info(() -> new ParameterizedMessage("{} Calculated potential scaled down capacity [{}] ",
                         msg,
-                        scaleDownDecision.get().requiredCapacity()));
+                        scaleDownDecisionResult.requiredCapacity()));
                     return new AutoscalingDeciderResult(context.currentCapacity(), reasonBuilder.setSimpleReason(msg).build());
                 }
             }
 
             long msLeftToScale = msLeftToDownScale(configuration);
             if (msLeftToScale <= 0) {
-                return scaleDownDecision.get();
+                return scaleDownDecisionResult;
             }
             TimeValue downScaleDelay = DOWN_SCALE_DELAY.get(configuration);
             logger.debug(() -> new ParameterizedMessage(
                 "not scaling down as the current scale down delay [{}] is not satisfied." +
                     " The last time scale down was detected [{}]. Calculated scaled down capacity [{}] ",
                 downScaleDelay.getStringRep(),
                 XContentElasticsearchExtension.DEFAULT_DATE_PRINTER.print(scaleDownDetected),
-                scaleDownDecision.get().requiredCapacity()));
+                scaleDownDecisionResult.requiredCapacity()));
             return new AutoscalingDeciderResult(
                 context.currentCapacity(),
                 reasonBuilder
@@ -586,6 +600,31 @@ public AutoscalingDeciderResult scale(Settings configuration, AutoscalingDecider
                     .build()));
     }
 
+    static AutoscalingCapacity ensureScaleDown(AutoscalingCapacity scaleDownResult, AutoscalingCapacity currentCapacity) {
+        if (currentCapacity == null || scaleDownResult == null) {
+            return null;
+        }
+        AutoscalingCapacity newCapacity = new AutoscalingCapacity(
+            new AutoscalingCapacity.AutoscalingResources(
+                currentCapacity.total().storage(),
+                ByteSizeValue.ofBytes(Math.min(scaleDownResult.total().memory().getBytes(), currentCapacity.total().memory().getBytes()))
+            ),
+            new AutoscalingCapacity.AutoscalingResources(
+                currentCapacity.node().storage(),
+                ByteSizeValue.ofBytes(Math.min(scaleDownResult.node().memory().getBytes(), currentCapacity.node().memory().getBytes()))
+            )
+        );
+        if (scaleDownResult.node().memory().getBytes() - newCapacity.node().memory().getBytes() > ACCEPTABLE_DIFFERENCE
+            || scaleDownResult.total().memory().getBytes() - newCapacity.total().memory().getBytes() > ACCEPTABLE_DIFFERENCE) {
+            logger.warn(
+                "scale down accidentally requested a scale up, auto-corrected; initial scaling [{}], corrected [{}]",
+                scaleDownResult,
+                newCapacity
+            );
+        }
+        return newCapacity;
+    }
+
     AutoscalingDeciderResult noScaleResultOrRefresh(MlScalingReason.Builder reasonBuilder,
                                                     boolean memoryTrackingStale,
                                                     AutoscalingDeciderResult potentialResult) {
@@ -842,8 +881,11 @@ Optional<AutoscalingDeciderResult> checkForScaleDown(List<NodeLoad> nodeLoads,
         // Or our largest job could be on a smaller node (meaning the same size tier but smaller nodes are possible).
         if (currentlyNecessaryTier < currentCapacity.getTier() || currentlyNecessaryNode < currentCapacity.getNode()) {
             NativeMemoryCapacity nativeMemoryCapacity = new NativeMemoryCapacity(
-                currentlyNecessaryTier,
-                currentlyNecessaryNode,
+                // Since we are in the `scaleDown` branch, we know jobs are running and we could be smaller
+                // If we have some weird rounding errors, it may be that the `currentlyNecessary` values are larger than
+                // current capacity. We never want to accidentally say "scale up" via a scale down.
+                Math.min(currentlyNecessaryTier, currentCapacity.getTier()),
+                Math.min(currentlyNecessaryNode, currentCapacity.getNode()),
                 // If our newly suggested native capacity is the same, we can use the previously stored jvm size
                 currentlyNecessaryNode == currentCapacity.getNode() ? currentCapacity.getJvmSize() : null);
             AutoscalingCapacity requiredCapacity = nativeMemoryCapacity.autoscalingCapacity(maxMachineMemoryPercent, useAuto);

x-pack/plugin/ml/src/main/java/org/elasticsearch/xpack/ml/autoscaling/NativeMemoryCapacity.java

@@ -12,6 +12,9 @@
 import org.elasticsearch.xpack.ml.utils.NativeMemoryCalculator;
 
 import java.util.Objects;
+import java.util.Optional;
+
+import static org.elasticsearch.xpack.ml.utils.NativeMemoryCalculator.dynamicallyCalculateJvmSizeFromNativeMemorySize;
 
 // Used for storing native memory capacity and then transforming it into an autoscaling capacity
 // which takes into account the whole node size
@@ -49,22 +52,26 @@ NativeMemoryCapacity merge(NativeMemoryCapacity nativeMemoryCapacity) {
         return this;
     }
 
-    AutoscalingCapacity autoscalingCapacity(int maxMemoryPercent, boolean useAuto) {
+    public AutoscalingCapacity autoscalingCapacity(int maxMemoryPercent, boolean useAuto) {
+        // We calculate the JVM size here first to ensure it stays the same given the rest of the calculations
+        final Long jvmSize = useAuto ?
+            Optional.ofNullable(this.jvmSize).orElse(dynamicallyCalculateJvmSizeFromNativeMemorySize(node)) :
+            null;
         // We first need to calculate the actual node size given the current native memory size.
         // This way we can accurately determine the required node size AND what the overall memory percentage will be
         long actualNodeSize = NativeMemoryCalculator.calculateApproxNecessaryNodeSize(node, jvmSize, maxMemoryPercent, useAuto);
         // We make the assumption that the JVM size is the same across the entire tier
         // This simplifies calculating the tier as it means that each node in the tier
         // will have the same dynamic memory calculation. And thus the tier is simply the sum of the memory necessary
         // times that scaling factor.
-        int memoryPercentForMl = (int)Math.floor(NativeMemoryCalculator.modelMemoryPercent(
+        double memoryPercentForMl = NativeMemoryCalculator.modelMemoryPercent(
             actualNodeSize,
             jvmSize,
             maxMemoryPercent,
             useAuto
-        ));
+        );
         double inverseScale = memoryPercentForMl <= 0 ? 0 : 100.0 / memoryPercentForMl;
-        long actualTier = (long)Math.ceil(tier * inverseScale);
+        long actualTier = Math.round(tier * inverseScale);
         return new AutoscalingCapacity(
             // Tier should always be AT LEAST the largest node size.
             // This Math.max catches any strange rounding errors or weird input.

x-pack/plugin/ml/src/main/java/org/elasticsearch/xpack/ml/utils/NativeMemoryCalculator.java

@@ -23,6 +23,7 @@
 
 public final class NativeMemoryCalculator {
 
+    private static final long STATIC_JVM_UPPER_THRESHOLD = ByteSizeValue.ofGb(2).getBytes();
     static final long MINIMUM_AUTOMATIC_NODE_SIZE = ByteSizeValue.ofGb(1).getBytes();
     private static final long OS_OVERHEAD = ByteSizeValue.ofMb(200L).getBytes();
 
@@ -80,15 +81,11 @@ public static long calculateApproxNecessaryNodeSize(long nativeMachineMemory, Lo
         if (useAuto) {
             // TODO utilize official ergonomic JVM size calculations when available.
             jvmSize = jvmSize == null ? dynamicallyCalculateJvmSizeFromNativeMemorySize(nativeMachineMemory) : jvmSize;
-            // We use a Math.floor here to ensure we have AT LEAST enough memory given rounding.
-            int modelMemoryPercent = (int)Math.floor(modelMemoryPercent(
-                nativeMachineMemory + jvmSize + OS_OVERHEAD,
-                jvmSize,
-                maxMemoryPercent,
-                true));
-            // We calculate the inverse percentage of `nativeMachineMemory + OS_OVERHEAD` as `OS_OVERHEAD` is always present
-            // on the native memory side and we need to account for it when we invert the model memory percentage
-            return Math.max((long)Math.ceil((100.0/modelMemoryPercent) * (nativeMachineMemory + OS_OVERHEAD)), MINIMUM_AUTOMATIC_NODE_SIZE);
+            // We haven't reached our 90% threshold, so, simply summing up the values is adequate
+            if ((jvmSize + OS_OVERHEAD)/(double)nativeMachineMemory > 0.1) {
+                return Math.max(nativeMachineMemory + jvmSize + OS_OVERHEAD, MINIMUM_AUTOMATIC_NODE_SIZE);
+            }
+            return Math.round((nativeMachineMemory/0.9));
         }
         return (long) ((100.0/maxMemoryPercent) * nativeMachineMemory);
     }
@@ -118,18 +115,11 @@ public static double modelMemoryPercent(long machineMemory, Long jvmSize, int ma
         return maxMemoryPercent;
     }
 
-    public static int modelMemoryPercent(long machineMemory, int maxMemoryPercent, boolean useAuto) {
-        return (int)Math.ceil(modelMemoryPercent(machineMemory,
-            null,
-            maxMemoryPercent,
-            useAuto));
-    }
-
-    private static long allowedBytesForMl(long machineMemory, Long jvmSize, int maxMemoryPercent, boolean useAuto) {
+    static long allowedBytesForMl(long machineMemory, Long jvmSize, int maxMemoryPercent, boolean useAuto) {
         if (useAuto && jvmSize != null) {
             // It is conceivable that there is a machine smaller than 200MB.
             // If the administrator wants to use the auto configuration, the node should be larger.
-            if (machineMemory - jvmSize < OS_OVERHEAD || machineMemory == 0) {
+            if (machineMemory - jvmSize <= OS_OVERHEAD || machineMemory == 0) {
                 return machineMemory / 100;
             }
             // This calculation is dynamic and designed to maximally take advantage of the underlying machine for machine learning
@@ -139,8 +129,8 @@ private static long allowedBytesForMl(long machineMemory, Long jvmSize, int maxM
             // 2GB node -> 66%
             // 16GB node -> 87%
             // 64GB node -> 90%
-            long memoryPercent = Math.min(90, (int)Math.ceil(((machineMemory - jvmSize - OS_OVERHEAD) / (double)machineMemory) * 100.0D));
-            return (long)(machineMemory * (memoryPercent / 100.0));
+            double memoryProportion = Math.min(0.90, (machineMemory - jvmSize - OS_OVERHEAD) / (double)machineMemory);
+            return Math.round(machineMemory * memoryProportion);
         }
 
         return (long)(machineMemory * (maxMemoryPercent / 100.0));
@@ -154,30 +144,33 @@ public static long allowedBytesForMl(long machineMemory, int maxMemoryPercent, b
     }
 
     // TODO replace with official ergonomic calculation
-    private static long dynamicallyCalculateJvmSizeFromNodeSize(long nodeSize) {
-        if (nodeSize < ByteSizeValue.ofGb(2).getBytes()) {
-            return (long) (nodeSize * 0.40);
+    public static long dynamicallyCalculateJvmSizeFromNodeSize(long nodeSize) {
+        // While the original idea here was to predicate on 2Gb, it has been found that the knot points of
+        // 2GB and 8GB cause weird issues where the JVM size will "jump the gap" from one to the other when
+        // considering true tier sizes in elastic cloud.
+        if (nodeSize < ByteSizeValue.ofMb(1280).getBytes()) {
+            return (long)(nodeSize * 0.40);
         }
         if (nodeSize < ByteSizeValue.ofGb(8).getBytes()) {
-            return (long) (nodeSize * 0.25);
+            return (long)(nodeSize * 0.25);
         }
-        return ByteSizeValue.ofGb(2).getBytes();
+        return STATIC_JVM_UPPER_THRESHOLD;
     }
 
-    private static long dynamicallyCalculateJvmSizeFromNativeMemorySize(long nativeMachineMemory) {
+    public static long dynamicallyCalculateJvmSizeFromNativeMemorySize(long nativeMachineMemory) {
         // See dynamicallyCalculateJvm the following JVM calculations are arithmetic inverses of JVM calculation
         //
         // Example: For < 2GB node, the JVM is 0.4 * total_node_size. This means, the rest is 0.6 the node size.
         // So, the `nativeAndOverhead` is == 0.6 * total_node_size => total_node_size = (nativeAndOverHead / 0.6)
         // Consequently jvmSize = (nativeAndOverHead / 0.6)*0.4 = nativeAndOverHead * 2/3
         long nativeAndOverhead = nativeMachineMemory + OS_OVERHEAD;
         if (nativeAndOverhead < (ByteSizeValue.ofGb(2).getBytes() * 0.60)) {
-            return (long) Math.ceil(nativeAndOverhead * (2.0 / 3.0));
+            return Math.round((nativeAndOverhead / 0.6) * 0.4);
         }
         if (nativeAndOverhead < (ByteSizeValue.ofGb(8).getBytes() * 0.75)) {
-            return (long) Math.ceil(nativeAndOverhead / 3.0);
+            return Math.round((nativeAndOverhead / 0.75) * 0.25);
         }
-        return ByteSizeValue.ofGb(2).getBytes();
+        return STATIC_JVM_UPPER_THRESHOLD;
     }
 
 }