diff --git a/expe_replay_feedback_KTH.ipynb b/expe_replay_feedback_KTH.ipynb
index 61290759fc419de576d3e7b3c36a7525773eede6..be90a386aa4e6f3513ed91fc8aa47c1ed7dcb6b3 100644
--- a/expe_replay_feedback_KTH.ipynb
+++ b/expe_replay_feedback_KTH.ipynb
@@ -14,7 +14,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -22,16 +22,18 @@
     "EXPE_DIR = \"out/expe_replay_KTH\"\n",
     "PF_folder = \"platform/KTH\"\n",
     "WL_folder = \"workload/KTH\"\n",
+    "WL_swf_path = f\"{WL_folder}/KTH-SP2.swf\"\n",
     "\n",
-    "# Original log start time\n",
+    "# Original log params\n",
+    "WL_URL = \"http://www.cs.huji.ac.il/labs/parallel/workload/l_kth_sp2/KTH-SP2-1996-2.1-cln.swf.gz\"\n",
     "WL_start_time = '1996-09-23 14:00:31 CEST'\n",
-    "WL_swf_path = f\"{WL_folder}/KTH-SP2.swf\"\n",
-    "WL_URL = \"http://www.cs.huji.ac.il/labs/parallel/workload/l_kth_sp2/KTH-SP2-1996-2.1-cln.swf.gz\"\n"
+    "timezone = \"Europe/Stockholm\"\n",
+    "begin_data_in_swf, end_data_in_swf = 25, 28500 # line number (counting from 1)\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -86,12 +88,12 @@
     "    plt.show()\n",
     "    plt.close(fig)\n",
     "\n",
-    "def ajust_timestamp_column(col, time_origin=WL_start_time_utc, timezone=\"Europe/Stockholm\"):\n",
+    "def ajust_timestamp_column(col, time_origin=WL_start_time_utc, timezone=timezone):\n",
     "    col = pd.to_datetime(col, unit='s', utc=True, origin=time_origin).dt.tz_convert(tz=timezone)\n",
     "    col = col.dt.tz_localize(None) # drop the time_zone informationn keeping local time\n",
     "    return col\n",
     "\n",
-    "def read_and_clean(jobs_file, time_origin=WL_start_time_utc, timezone=\"Europe/Stockholm\"):\n",
+    "def read_and_clean(jobs_file, time_origin=WL_start_time_utc, timezone=timezone):\n",
     "    jobs = pd.read_csv(jobs_file, dtype={\"job_id\":\"str\"})\n",
     "\n",
     "    # Clean job_id column and set it as index (job_ids can be '45:s1', indicating a session)\n",
@@ -156,9 +158,36 @@
     "    l = length(ref).total_seconds()\n",
     "    return (l + mean_lateness(df, ref)) / l\n",
     "\n",
+    "def process_for_util(m_state, timestamps_to_add):\n",
+    "    m_state = m_state.copy()[[\"time\", \"nb_computing\"]]\n",
+    "    m_state.time = ajust_timestamp_column(m_state.time)\n",
+    "\n",
+    "    # Add in the file the timestamps defining our time window, if necessary\n",
+    "    for timestamp in timestamps_to_add:\n",
+    "        prev_el = m_state[m_state.time <= timestamp].tail(1)\n",
+    "        prev_time, prev_nb_comp = prev_el.time.iloc[0], prev_el.nb_computing.iloc[0]  \n",
+    "        if prev_time != timestamp:\n",
+    "            m_state.loc[len(m_state)] = [timestamp, prev_nb_comp]\n",
+    "            m_state.sort_values(by=\"time\", inplace=True)\n",
+    "\n",
+    "    # Calculate column area\n",
+    "    m_state[\"timediff\"] =  m_state.time.astype(\"int\").diff(periods=-1) / 10**9 # in ns, convert to s\n",
+    "    m_state[\"area\"] = - m_state.nb_computing * m_state.timediff\n",
+    "    return m_state\n",
+    "\n",
+    "def mean_util_between(m_state, start, end):\n",
+    "    \"\"\"Calculate the mean platform utilization bewteen two dates\"\"\"\n",
+    "    nb_machines = m_state.nb_computing.iloc[0] + m_state.nb_idle.iloc[0]\n",
+    "    m_state = process_for_util(m_state, [start, end])\n",
+    "    win = m_state[(m_state.time >= start) & (m_state.time < end)]\n",
+    "    mean_utilization = win.area.sum() / (end-start).total_seconds() / nb_machines * 100 # in %\n",
+    "    return mean_utilization\n",
+    "\n",
     "# Charge the reference XP in memory\n",
     "if os.path.exists(f\"{EXPE_DIR}/rigid_FCFS/_jobs.csv\"):\n",
-    "    WL_rigid = read_and_clean(f\"{EXPE_DIR}/rigid_FCFS/_jobs.csv\")"
+    "    WL_rigid = read_and_clean(f\"{EXPE_DIR}/rigid_FCFS/_jobs.csv\")\n",
+    "if os.path.exists(WL_swf_path):\n",
+    "    WL_swf = pd.read_csv(WL_swf_path, header=begin_data_in_swf-2, delim_whitespace=True, names=header)"
    ]
   },
   {
@@ -2911,23 +2940,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1996-10-13 22:00:00\n"
-     ]
-    },
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x7faa8018ca00>"
+       "<matplotlib.legend.Legend at 0x7fe9adfc1030>"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -2964,7 +2986,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -3160,99 +3182,99 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>RIGID_EASY_speed*2</th>\n",
-       "      <td>35.887547</td>\n",
-       "      <td>35.797331</td>\n",
-       "      <td>35.689020</td>\n",
-       "      <td>35.645236</td>\n",
-       "      <td>35.645501</td>\n",
+       "      <td>35.868651</td>\n",
+       "      <td>36.021682</td>\n",
+       "      <td>35.916644</td>\n",
+       "      <td>35.903046</td>\n",
+       "      <td>35.867069</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>RIGID_EASY_infra*2</th>\n",
-       "      <td>36.347226</td>\n",
-       "      <td>36.292995</td>\n",
-       "      <td>36.185383</td>\n",
-       "      <td>36.106212</td>\n",
-       "      <td>36.110227</td>\n",
+       "      <td>36.414774</td>\n",
+       "      <td>36.297137</td>\n",
+       "      <td>36.248243</td>\n",
+       "      <td>36.146493</td>\n",
+       "      <td>36.147661</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>A0_EASY_infra*2</th>\n",
-       "      <td>45.570763</td>\n",
-       "      <td>42.388043</td>\n",
-       "      <td>41.296250</td>\n",
-       "      <td>40.516793</td>\n",
-       "      <td>40.025067</td>\n",
+       "      <td>45.586585</td>\n",
+       "      <td>42.370848</td>\n",
+       "      <td>41.287575</td>\n",
+       "      <td>40.514534</td>\n",
+       "      <td>40.016213</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>A0_EASY_speed*2</th>\n",
-       "      <td>46.349574</td>\n",
-       "      <td>44.085695</td>\n",
-       "      <td>42.216274</td>\n",
-       "      <td>41.536155</td>\n",
-       "      <td>40.381266</td>\n",
+       "      <td>46.361859</td>\n",
+       "      <td>44.338688</td>\n",
+       "      <td>42.435090</td>\n",
+       "      <td>41.709789</td>\n",
+       "      <td>40.536177</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>A0_FCFS</th>\n",
-       "      <td>59.298483</td>\n",
-       "      <td>59.910821</td>\n",
-       "      <td>61.651191</td>\n",
-       "      <td>61.328241</td>\n",
-       "      <td>61.981249</td>\n",
+       "      <td>60.116361</td>\n",
+       "      <td>60.339305</td>\n",
+       "      <td>62.227581</td>\n",
+       "      <td>62.013051</td>\n",
+       "      <td>62.601251</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>RIGID_FCFS</th>\n",
-       "      <td>64.664654</td>\n",
-       "      <td>65.241432</td>\n",
-       "      <td>66.400563</td>\n",
-       "      <td>67.615936</td>\n",
-       "      <td>69.182069</td>\n",
+       "      <td>65.976655</td>\n",
+       "      <td>66.375463</td>\n",
+       "      <td>67.519651</td>\n",
+       "      <td>68.693917</td>\n",
+       "      <td>70.195604</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>RIGID_EASY</th>\n",
-       "      <td>69.405290</td>\n",
-       "      <td>69.516805</td>\n",
-       "      <td>69.282133</td>\n",
-       "      <td>69.291727</td>\n",
-       "      <td>69.461227</td>\n",
+       "      <td>70.017454</td>\n",
+       "      <td>70.357278</td>\n",
+       "      <td>70.361521</td>\n",
+       "      <td>70.315827</td>\n",
+       "      <td>70.281021</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>A0_EASY</th>\n",
-       "      <td>75.770447</td>\n",
-       "      <td>77.268074</td>\n",
-       "      <td>74.474063</td>\n",
-       "      <td>73.675082</td>\n",
-       "      <td>74.242252</td>\n",
+       "      <td>75.651429</td>\n",
+       "      <td>77.841024</td>\n",
+       "      <td>75.066832</td>\n",
+       "      <td>74.176669</td>\n",
+       "      <td>74.723369</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>A0_EASY_infra/2</th>\n",
-       "      <td>85.226908</td>\n",
-       "      <td>85.771994</td>\n",
-       "      <td>85.687589</td>\n",
-       "      <td>86.229928</td>\n",
-       "      <td>86.545623</td>\n",
+       "      <td>85.926112</td>\n",
+       "      <td>86.718128</td>\n",
+       "      <td>86.869955</td>\n",
+       "      <td>87.138485</td>\n",
+       "      <td>87.493000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>A0_EASY_speed/2</th>\n",
-       "      <td>86.970224</td>\n",
-       "      <td>87.924840</td>\n",
-       "      <td>88.486544</td>\n",
-       "      <td>88.852350</td>\n",
-       "      <td>89.075348</td>\n",
+       "      <td>89.065374</td>\n",
+       "      <td>88.628941</td>\n",
+       "      <td>89.800876</td>\n",
+       "      <td>90.152986</td>\n",
+       "      <td>90.585759</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>RIGID_EASY_infra/2</th>\n",
-       "      <td>88.607881</td>\n",
-       "      <td>88.686264</td>\n",
-       "      <td>88.743879</td>\n",
-       "      <td>89.124205</td>\n",
-       "      <td>89.204742</td>\n",
+       "      <td>90.544853</td>\n",
+       "      <td>91.089422</td>\n",
+       "      <td>91.304335</td>\n",
+       "      <td>91.516202</td>\n",
+       "      <td>91.677485</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>RIGID_EASY_speed/2</th>\n",
-       "      <td>90.649025</td>\n",
-       "      <td>91.224316</td>\n",
-       "      <td>91.603095</td>\n",
-       "      <td>92.240658</td>\n",
-       "      <td>92.493404</td>\n",
+       "      <td>93.597870</td>\n",
+       "      <td>92.545941</td>\n",
+       "      <td>93.090542</td>\n",
+       "      <td>94.410882</td>\n",
+       "      <td>94.483753</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -3260,18 +3282,18 @@
       ],
       "text/plain": [
        "                           4m         5m         6m         7m         8m\n",
-       "RIGID_EASY_speed*2  35.887547  35.797331  35.689020  35.645236  35.645501\n",
-       "RIGID_EASY_infra*2  36.347226  36.292995  36.185383  36.106212  36.110227\n",
-       "A0_EASY_infra*2     45.570763  42.388043  41.296250  40.516793  40.025067\n",
-       "A0_EASY_speed*2     46.349574  44.085695  42.216274  41.536155  40.381266\n",
-       "A0_FCFS             59.298483  59.910821  61.651191  61.328241  61.981249\n",
-       "RIGID_FCFS          64.664654  65.241432  66.400563  67.615936  69.182069\n",
-       "RIGID_EASY          69.405290  69.516805  69.282133  69.291727  69.461227\n",
-       "A0_EASY             75.770447  77.268074  74.474063  73.675082  74.242252\n",
-       "A0_EASY_infra/2     85.226908  85.771994  85.687589  86.229928  86.545623\n",
-       "A0_EASY_speed/2     86.970224  87.924840  88.486544  88.852350  89.075348\n",
-       "RIGID_EASY_infra/2  88.607881  88.686264  88.743879  89.124205  89.204742\n",
-       "RIGID_EASY_speed/2  90.649025  91.224316  91.603095  92.240658  92.493404"
+       "RIGID_EASY_speed*2  35.868651  36.021682  35.916644  35.903046  35.867069\n",
+       "RIGID_EASY_infra*2  36.414774  36.297137  36.248243  36.146493  36.147661\n",
+       "A0_EASY_infra*2     45.586585  42.370848  41.287575  40.514534  40.016213\n",
+       "A0_EASY_speed*2     46.361859  44.338688  42.435090  41.709789  40.536177\n",
+       "A0_FCFS             60.116361  60.339305  62.227581  62.013051  62.601251\n",
+       "RIGID_FCFS          65.976655  66.375463  67.519651  68.693917  70.195604\n",
+       "RIGID_EASY          70.017454  70.357278  70.361521  70.315827  70.281021\n",
+       "A0_EASY             75.651429  77.841024  75.066832  74.176669  74.723369\n",
+       "A0_EASY_infra/2     85.926112  86.718128  86.869955  87.138485  87.493000\n",
+       "A0_EASY_speed/2     89.065374  88.628941  89.800876  90.152986  90.585759\n",
+       "RIGID_EASY_infra/2  90.544853  91.089422  91.304335  91.516202  91.677485\n",
+       "RIGID_EASY_speed/2  93.597870  92.545941  93.090542  94.410882  94.483753"
       ]
      },
      "metadata": {},
@@ -3296,40 +3318,18 @@
     "\n",
     "throughput, mean_util = {}, {}\n",
     "\n",
-    "def process_for_util(m_state):\n",
-    "    m_state = m_state.copy()[[\"time\", \"nb_computing\"]]\n",
-    "    m_state.time = ajust_timestamp_column(m_state.time)\n",
-    "\n",
-    "    # Add in the file the timestamps defining our time window, if necessary\n",
-    "    for timestamp in [start_metrics] + end_metrics:\n",
-    "        prev_el = m_state[m_state.time <= timestamp].tail(1)\n",
-    "        prev_time, prev_nb_comp = prev_el.time.iloc[0], prev_el.nb_computing.iloc[0]  \n",
-    "        if prev_time != timestamp:\n",
-    "            m_state.loc[len(m_state)] = [timestamp, prev_nb_comp]\n",
-    "            m_state.sort_values(by=\"time\", inplace=True)\n",
-    "\n",
-    "    # Calculate column area\n",
-    "    m_state[\"timediff\"] =  m_state.time.astype(\"int\").diff(periods=-1) / 10**9 # in ns, convert to s\n",
-    "    m_state[\"area\"] = - m_state.nb_computing * m_state.timediff\n",
-    "    return m_state\n",
-    "\n",
-    "\n",
     "for name, path in data.items():\n",
     "    js = JobSet.from_csv(f\"{path}/_jobs.csv\")\n",
-    "    m_state = pd.read_csv(f\"{path}/_machine_states.csv\")\n",
-    "    nb_machines = m_state.nb_computing.iloc[0] + m_state.nb_idle.iloc[0]\n",
     "\n",
     "    f_times = ajust_timestamp_column(js.df.finish_time)\n",
-    "    m_state = process_for_util(m_state)\n",
     "    throughput[name], mean_util[name] = [], []\n",
     "\n",
     "    for end in end_metrics:\n",
     "        thru = f_times[(f_times >= start_metrics) & (f_times < end)].count()\n",
     "        throughput[name].append(thru / nb_days_between(start_metrics, end) )\n",
     "\n",
-    "        win = m_state[(m_state.time >= start_metrics) & (m_state.time < end)]\n",
-    "        mean_utilization = win.area.sum() / (end-start_metrics).total_seconds() / nb_machines * 100 # in %\n",
-    "        mean_util[name].append(mean_utilization)\n",
+    "        m_state = pd.read_csv(f\"{path}/_machine_states.csv\")\n",
+    "        mean_util[name].append(mean_util_between(m_state, start_metrics, end))\n",
     "\n",
     "# Tables\n",
     "throughput = pd.DataFrame.from_dict(throughput, orient=\"index\", columns=[\"4m\", \"5m\", \"6m\", \"7m\", \"8m\"])\n",
@@ -3342,12 +3342,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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       "text/plain": [
        "<Figure size 1600x500 with 2 Axes>"
       ]
diff --git a/expe_replay_feedback_SDSC.ipynb b/expe_replay_feedback_SDSC.ipynb
index e1e182bd5ea1288092069d8be6ee5f3c388ae6a5..a9084197d5fa730877351468c4b25140612cc884 100644
--- a/expe_replay_feedback_SDSC.ipynb
+++ b/expe_replay_feedback_SDSC.ipynb
@@ -14,7 +14,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -33,7 +33,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -215,7 +215,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -669,7 +669,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -12351,7 +12351,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {