Added script which produces plotable data.

analysis.py

+#!/usr/bin/env python3
+
+import argparse
+import math
+
+def calc_utilization(tasks):
+    util = 0
+    for task in tasks:
+        util += task['wcet'] / task['period']
+    return util
+
+def main():
+    parser = argparse.ArgumentParser(description='Generate plotable results from the experiments.')
+    parser.add_argument('results', type=str, nargs='+', help='file containing experiment results.')
+
+    args = parser.parse_args()
+
+    out = {}
+
+    for result in args.results:
+        with open(result, 'r') as result_file:
+            tasks = {}
+            instances = {}
+
+            for line in result_file:
+                tokens = line.strip().split(' ')
+                if tokens[0] == 'task':
+                    #task 0 wcet 5 period 39 deadline 32 executions 15480
+
+                    task_id = int(tokens[1])
+                    wcet = int(tokens[3])
+                    period = int(tokens[5])
+                    deadline = int(tokens[7])
+                    executions = int(tokens[9])
+
+                    tasks[task_id] = { 'id': task_id, 'wcet': wcet, 'period': period, 'deadline': deadline, 'executions': executions }
+                    instances[task_id] = { 's2s':0, 'e2e':0, 'misses':0, 'response':0 }
+
+                elif tokens[0] == 'instance':
+                    #instance 12 start 478 end 487 core_id 0
+
+                    start = int(tokens[3])
+                    end = int(tokens[5])
+
+                    if start == 0 and end == 0:
+                        # Ignore not executed tasks, not realy that clean but it
+                        # happens hardly, so nobody should care...
+                        tasks[task_id]['executions'] -= 1
+                        continue
+
+                    release = start - (start % tasks[task_id]['period'])
+                    abs_deadline = release + tasks[task_id]['deadline']
+
+                    instances[task_id]['s2s'] += start - release
+                    instances[task_id]['e2e'] += end - release
+                    instances[task_id]['response'] += end - release
+                    if end > abs_deadline:
+                        instances[task_id]['misses'] += 1
+                else:
+                    continue
+
+        util = calc_utilization(tasks.values())
+        s2s = 0
+        e2e = 0
+        misses = 0
+        response = 0
+        for task_id, task in instances.items():
+            s2s += math.sqrt(task['s2s'] / tasks[task_id]['executions'])
+            e2e += math.sqrt(task['e2e'] / tasks[task_id]['executions'])
+            misses += task['misses']
+            response += (task['response'] / tasks[task_id]['executions']) / tasks[task_id]['period']
+
+        s2s /= len(tasks)
+        e2e /= len(tasks)
+        response /= len(tasks)
+
+        outkey = round(util, 2)
+
+        try:
+            out[outkey].append((s2s, e2e, misses, response))
+        except KeyError:
+            out[outkey] = [(s2s, e2e, misses, response)]
+
+    with open('analysis/s2s.dat', 'w') as s2sfile:
+        for key, value in out.items():
+            for elem in value:
+                print('{}; {}'.format(key, elem[0]), file=s2sfile)
+
+    with open('analysis/e2e.dat', 'w') as e2efile:
+        for key, value in out.items():
+            for elem in value:
+                print('{}; {}'.format(key, elem[1]), file=e2efile)
+
+    with open('analysis/misses.dat', 'w') as missesfile:
+        for key, value in out.items():
+            for elem in value:
+                print('{}; {}'.format(key, elem[2]), file=missesfile)
+
+    with open('analysis/response.dat', 'w') as responsefile:
+        for key, value in out.items():
+            for elem in value:
+                print('{}; {}'.format(key, elem[3]), file=responsefile)
+
+if __name__ == '__main__':
+    main()