Handle shutdown

Adds a Shutdown Hook. Uses parsed configurations to build a deployment and to tear it down. Be aware of Gradle: Control + C kills the gradle client before the execution of the hook and therefore cant be used to test this. To test this you need to start the build application (by java -jar, etc...). Additionally for this you have to change the paths in the TheodoliteYamlExecutor and the KubernetesBenchmark

TODOS:

• Depends on merging evaluation first
• Ask if and how the Kafka lag export should be resettet
• moved to #184 (closed)
• move path from KubenetesBenchmark toplevel moved to #185 (closed)
• merge upstream theodolite-kotlin

slope-evaluator/app/main.py

+from fastapi import FastAPI,Request
+import trend_slope_computer as trend_slope_computer
+import logging
+import os
+import pandas as pd
+import json
+import sys
+
+app = FastAPI()
+
+logging.basicConfig(stream=sys.stdout,
+                    format="%(asctime)s %(levelname)s %(name)s: %(message)s")
+logger = logging.getLogger("API")
+
+
+if os.getenv('LOG_LEVEL') == 'INFO':
+    logger.setLevel(logging.INFO)
+elif os.getenv('LOG_LEVEL') == 'WARNING':
+    logger.setLevel(logging.WARNING)
+elif os.getenv('LOG_LEVEL') == 'DEBUG':
+    logger.setLevel((logging.DEBUG))
+
+def execute(results, threshold, warmup):
+    d = []
+    for result in results:
+        group = result['metric']['group']
+        for value in result['values']:
+            d.append({'group': group, 'timestamp': int(
+                value[0]), 'value': int(value[1]) if value[1] != 'NaN' else 0})
+
+    df = pd.DataFrame(d)
+
+    logger.info(df)
+    try:
+        trend_slope = trend_slope_computer.compute(df, warmup)
+    except Exception as e:
+        err_msg = 'Computing trend slope failed'
+        logger.exception(err_msg)
+        logger.error('Mark this subexperiment as not successful and continue benchmark')
+        return False
+
+    logger.info("Trend Slope: %s", trend_slope)
+
+    return trend_slope < threshold
+
+@app.post("/evaluate-slope",response_model=bool)
+async def evaluate_slope(request: Request):
+    data = json.loads(await request.body())
+    return execute(data['total_lag'], data['threshold'], data['warmup'])
+
+logger.info("Slope evaluator is online")
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