move to standalone plugin

2026-08-23 13:44:39 +02:00
parent f227d054b8
commit 4188bea061
65 changed files with 14093 additions and 77 deletions
--- a/k8s-dvfs/src/main/groovy/recreationaltech/plugin/K8sLinearFitRuntimeEstimator.groovy
+++ b/k8s-dvfs/src/main/groovy/recreationaltech/plugin/K8sLinearFitRuntimeEstimator.groovy
@@ -0,0 +1,99 @@
+package recreationaltech.plugin
+
+import groovy.util.logging.Slf4j
+
+import java.nio.file.Files
+import java.nio.file.Path
+
+/**
+ * Provides runtime estimates for tasks based on task-name and input file size
+ */
+@Slf4j
+class K8sLinearFitRuntimeEstimator extends K8sRuntimeEstimator {
+
+    class Function {
+        private double m
+        private double n
+
+        Function(double m, double n) {
+            this.m = m
+            this.n = n
+        }
+
+        double estimate(long x) {
+            return m * (double)x + n
+        }
+    }
+
+    HashMap<String, Function> estimators;
+
+    /**
+     * Initialize the estimator with data recorded by K8sRuntimeRecorder
+     * @param dataFilePath
+     */
+    K8sLinearFitRuntimeEstimator(String dataFilePath) {
+        def data = parseDataFile(dataFilePath)
+        fit(data)
+    }
+
+    /**
+     * Initializes the runtime estimator with statically known data.
+     * @param data Map from task name to list of recordings, where each recording is a tuple (input-size, runtime-in-ms)
+     */
+    K8sLinearFitRuntimeEstimator(HashMap<String, ArrayList<Tuple2<Long, Long>>> data) {
+        fit(data)
+    }
+
+    /**
+     * Returns an estimation of the task runtime in milliseconds
+     * @param taskName the name of the task
+     * @param inputSize the total input size in bytes
+     * @return estimated task runtime in milliseconds OR infinity if the task is unknown.
+     */
+    double estimate(String taskName, long inputSize) {
+        Function estimator = estimators.get(taskName)
+        if (estimator == null) {
+            //log.warn "[K8s] Unable to estimate take ${taskName}: unknown task"
+            return Double.POSITIVE_INFINITY
+        }
+        return estimator.estimate(inputSize)
+    }
+
+    private void fit(HashMap<String, ArrayList<Tuple2<Long, Long>>> data) {
+        estimators = new HashMap<>()
+        for (Map.Entry<String, ArrayList<Tuple2<Long, Long>>> t : data) {
+            Function f = fit(t.value)
+            estimators.put(t.key, f)
+        }
+    }
+
+    /**
+     * Uses linear regression to fit a linear function (y = m * x + n) to the provided observations
+     * @param observations list of tuples (input size, runtime in ms)
+     * @return linear function fitted to the input
+     */
+    private Function fit(ArrayList<Tuple2<Long, Long>> observations) throws IllegalArgumentException {
+        int n = observations.size()
+        if (n > 1) {
+            double sumX = 0, sumY = 0, sumXY = 0, sumX2 = 0
+            for (Tuple2<Long, Long> o : observations) {
+                double x = (double) o.get(0)
+                double y = (double) o.get(1)
+                sumX += x
+                sumY += y
+                sumXY += x * y
+                sumX2 += x * x
+            }
+            double m = (n * sumXY - sumX * sumY) / (n * sumX2 - sumX * sumX)
+            return new Function(
+                m,
+                (sumY - m * sumX) / n
+            )
+        } else if (n == 1) {
+            // Special case: We only have 1 measurement. We will just assume that the runtime is constant,
+            // because in our observed data, it is.
+            return new Function(0.0, (double) observations[0].get(1))
+        }
+        throw new IllegalArgumentException("requires at least 1 observation")
+    }
+}