[ML] Improve memory estimation methods accuracy in TrainedModelAssignmentRebalancer and related classes

docs/changelog/133930.yaml

@@ -0,0 +1,6 @@
+pr: 133930
+summary: Improve memory estimation methods accuracy in `TrainedModelAssignmentRebalancer`
+  and related classes
+area: Machine Learning
+type: bug
+issues: []

x-pack/plugin/ml/src/main/java/org/elasticsearch/xpack/ml/inference/assignment/TrainedModelAssignmentRebalancer.java

@@ -406,7 +406,26 @@ private Optional<String> explainAssignment(
             return Optional.of(load.getError());
         }
 
-        if (deployment.memoryBytes() > assignmentPlan.getRemainingNodeMemory(node.getId())) {
+        // Find how many allocations already exist on this node
+        int existingAllocationsOnNode = assignmentPlan.assignments(deployment)
+            .map(
+                assignments -> assignments.getOrDefault(
+                    assignments.keySet().stream().filter(n -> n.id().equals(node.getId())).findFirst().orElse(null),
+                    0
+                )
+            )
+            .orElse(0);
+
+        // Calculate how many allocations remain to be assigned
+        int unassignedAllocations = deployment.allocations() - assignmentPlan.totalAllocations(deployment);
+
+        // Check if there's enough memory for additional allocations
+        long additionalMemory = deployment.estimateAdditionalMemoryUsageBytes(
+            existingAllocationsOnNode,
+            existingAllocationsOnNode + unassignedAllocations
+        );
+        long availableMemory = assignmentPlan.getRemainingNodeMemory(node.getId());
+        if (additionalMemory > availableMemory) {
             // If any ML processes are running on a node we require some space to load the shared libraries.
             // So if none are currently running then this per-node overhead must be added to the requirement.
             // From node load we know if we had any jobs or models assigned before the rebalance.

x-pack/plugin/ml/src/main/java/org/elasticsearch/xpack/ml/inference/assignment/planning/AssignmentPlan.java

@@ -107,7 +107,7 @@ public long estimateMemoryUsageBytes(int allocations) {
             );
         }
 
-        long estimateAdditionalMemoryUsageBytes(int allocationsOld, int allocationsNew) {
+        public long estimateAdditionalMemoryUsageBytes(int allocationsOld, int allocationsNew) {
             return StartTrainedModelDeploymentAction.estimateMemoryUsageBytes(
                 modelId,
                 memoryBytes,
@@ -308,7 +308,7 @@ private Quality computeQuality() {
                     Node n = nodeAllocations.getKey();
                     weighedAllocationsScore += (1 + 0.1 * (m.currentAllocationsByNodeId().containsKey(n.id()) ? 1 : 0)) * modelAssignments
                         .get(n);
-                    memoryScore -= (nodeAllocations.getValue() > 0 ? m.memoryBytes() : 0);
+                    memoryScore -= (nodeAllocations.getValue() > 0 ? m.estimateMemoryUsageBytes(nodeAllocations.getValue()) : 0);
                 }
             }
         }

x-pack/plugin/ml/src/main/java/org/elasticsearch/xpack/ml/inference/assignment/planning/LinearProgrammingPlanSolver.java

@@ -81,7 +81,7 @@ class LinearProgrammingPlanSolver {
         long maxNodeMemory = nodes.stream().map(Node::availableMemoryBytes).max(Long::compareTo).orElse(0L);
         this.deployments = deployments.stream()
             // Filter out models that are not already assigned and do not fit on any node
-            .filter(m -> m.currentAllocationsByNodeId().isEmpty() == false || m.memoryBytes() <= maxNodeMemory)
+            .filter(m -> m.currentAllocationsByNodeId().isEmpty() == false || m.minimumMemoryRequiredBytes() <= maxNodeMemory)
             // Also filter out models whose threads per allocation are more than the max node cores
             .filter(m -> m.threadsPerAllocation() <= maxNodeCores)
             .toList();

x-pack/plugin/ml/src/test/java/org/elasticsearch/xpack/ml/inference/assignment/TrainedModelAssignmentRebalancerTests.java

@@ -1198,6 +1198,64 @@ public void testCopyAssignments() {
         assertThat(deployment2Assignments.get().get(node2), equalTo(1));
     }
 
+    public void testRebalance_GivenDeploymentWithMemoryRequirements_ConsidersNativeExecutableOverhead() {
+        // Create a node with just enough memory to fit the model plus native executable overhead
+        long modelMemory = ByteSizeValue.ofMb(200).getBytes();
+        long memoryOverhead = ByteSizeValue.ofMb(240).getBytes();
+        long nodeMemory = memoryOverhead + modelMemory * 2 + MachineLearning.NATIVE_EXECUTABLE_CODE_OVERHEAD.getBytes();
+
+        DiscoveryNode node = buildNode("node-1", nodeMemory, 4);
+
+        String deploymentId = "model-with-overhead-test";
+        StartTrainedModelDeploymentAction.TaskParams taskParams = normalPriorityParams(deploymentId, deploymentId, modelMemory, 1, 1);
+
+        TrainedModelAssignmentMetadata currentMetadata = TrainedModelAssignmentMetadata.Builder.empty().build();
+        Map<DiscoveryNode, NodeLoad> nodeLoads = new HashMap<>();
+
+        // This node has no jobs or models yet, so the overhead should be accounted for
+        nodeLoads.put(node, NodeLoad.builder("node-1").setMaxMemory(nodeMemory).build());
+
+        TrainedModelAssignmentMetadata result = new TrainedModelAssignmentRebalancer(
+            currentMetadata,
+            nodeLoads,
+            Map.of(List.of(), List.of(node)),
+            Optional.of(new CreateTrainedModelAssignmentAction.Request(taskParams, null)),
+            1
+        ).rebalance().build();
+
+        // Verify the deployment was successful
+        TrainedModelAssignment assignment = result.getDeploymentAssignment(deploymentId);
+        assertThat(assignment, is(notNullValue()));
+        assertThat(assignment.getAssignmentState(), equalTo(AssignmentState.STARTING));
+        assertThat(assignment.getNodeRoutingTable(), is(aMapWithSize(1)));
+        assertThat(assignment.getNodeRoutingTable(), hasKey("node-1"));
+        assertThat(assignment.getReason().isPresent(), is(false));
+
+        // Now try with a node that has slightly less memory - this should fail
+        long insufficientNodeMemory = nodeMemory - ByteSizeValue.ofMb(21).getBytes();
+        DiscoveryNode insufficientNode = buildNode("node-2", insufficientNodeMemory, 4);
+
+        Map<DiscoveryNode, NodeLoad> insufficientNodeLoads = Map.of(
+            insufficientNode,
+            NodeLoad.builder("node-2").setMaxMemory(insufficientNodeMemory).build()
+        );
+
+        TrainedModelAssignmentMetadata insufficientResult = new TrainedModelAssignmentRebalancer(
+            TrainedModelAssignmentMetadata.Builder.empty().build(),
+            insufficientNodeLoads,
+            Map.of(List.of(), List.of(insufficientNode)),
+            Optional.of(new CreateTrainedModelAssignmentAction.Request(taskParams, null)),
+            1
+        ).rebalance().build();
+
+        TrainedModelAssignment insufficientAssignment = insufficientResult.getDeploymentAssignment(deploymentId);
+        assertThat(insufficientAssignment, is(notNullValue()));
+        assertThat(insufficientAssignment.getAssignmentState(), equalTo(AssignmentState.STARTING));
+        assertThat(insufficientAssignment.getNodeRoutingTable(), is(anEmptyMap()));
+        assertThat(insufficientAssignment.getReason().isPresent(), is(true));
+        assertThat(insufficientAssignment.getReason().get(), containsString("insufficient available memory"));
+    }
+
     private static StartTrainedModelDeploymentAction.TaskParams lowPriorityParams(String deploymentId, long modelSize) {
         return lowPriorityParams(deploymentId, deploymentId, modelSize);
     }