diff --git a/figure_8/figure8-abcd-only.py b/figure_8/figure8-abcd-only.py
new file mode 100755
index 00000000..bc9e3cb8
--- /dev/null
+++ b/figure_8/figure8-abcd-only.py
@@ -0,0 +1,170 @@
+#!/usr/bin/env python3
+# coding: utf-8
+
+import os
+import json
+import pandas as pd
+import findspark
+findspark.init()
+import pyspark
+import pyspark.sql
+import sys
+import gzip
+from pyspark import AccumulatorParam
+from pyspark.sql.functions import lit
+from pyspark.sql import Window
+from pyspark.sql.types import *
+from decimal import *
+
+if len(sys.argv) is not 4:
+    print(sys.argv[0] + " {cluster} {tmpdir} {maxram}")
+    sys.exit()
+
+cluster=sys.argv[1]
+
+spark = pyspark.sql.SparkSession.builder \
+  .appName("task_slowdown") \
+  .config("spark.driver.maxResultSize", sys.argv[3]) \
+  .config("spark.local.dir", sys.argv[2]) \
+  .config("spark.driver.memory", sys.argv[3]) \
+  .getOrCreate()
+sc = spark.sparkContext
+
+# READING INSTANCE EVENTS DATA
+dfpath = "/home/claudio/google_2019/instance_events/" + cluster + "/" + cluster + "_instance_events*.json.gz"
+df = spark.read.json(dfpath)
+
+### TESTING ONLY!
+df = df.limit(50000)
+
+try:
+    df["collection_type"] = df["collection_type"].cast(ByteType())
+except:
+    df = df.withColumn("collection_type", lit(None).cast(ByteType()))
+
+df = df.rdd \
+    .filter(lambda x: x.time is not None and x.type is not None and x.machine_id is not None and
+            x.instance_index is not None and x.collection_id is not None and x.resource_request is not None and
+            x.resource_request.cpus is not None and x.resource_request.memory is not None) \
+    .map(lambda x: [tabid(x), int(x.time), int(x.type),
+        Decimal(x.resource_request.cpus), Decimal(x.resource_request.memory), x.machine_id]) \
+    .toDF(["id", "time", "type", "rcpu", "rram", "mid"])
+
+# READING MACHINE EVENTS DATA, sort them and save them as broadcast variable
+print("Starting to read machine events...")
+dfm = pd.read_csv("~/google_2019/machine_events/" + cluster + "_machine_events.csv", converters={
+    'time': lambda x: -1 if x == '' else int(x),
+    'machine_id': lambda x: str(x),
+    'capacity.cpus': lambda x: -1 if x == '' else Decimal(x),
+    'capacity.memory': lambda x: -1 if x == '' else Decimal(x)})
+print("Dropping remove events...")
+dfm = dfm[(dfm.type!=2)&(dfm.time!=-1)&(dfm["capacity.cpus"]!=-1)&(dfm["capacity.memory"]!=-1)]
+print("Dropping missing data events...")
+dfm = dfm[dfm.missing_data_reason.isnull()]
+print("Projecting on useful columns...")
+dfm = dfm[["time", "machine_id", "capacity.cpus", "capacity.memory"]]
+print("Sorting by time...")
+dfm = dfm.sort_values(by=["machine_id", "time"])
+print("Converting to broadcast variable...")
+dfm = sc.broadcast([tuple(r) for r in dfm.to_numpy()])
+print("Done with machine events.")
+
+def get_machine_time_resources(machine_id, time):
+    def aux(i, j):
+        if i == j:
+            return dfm.value[i] if dfm.value[i][1] == machine_id else None
+        elif i + 1 == j:
+            if dfm.value[i][1] == machine_id:
+                return dfm.value[i]
+            elif dfm.value[j][1] == machine_id:
+                return dfm.value[j]
+            else:
+                return None
+
+        mid = (i + j) // 2
+
+        if dfm.value[mid][1] > machine_id:
+            return aux(i, mid - 1)
+        elif dfm.value[mid][1] < machine_id:
+            return aux(mid + 1, j)
+        elif dfm.value[mid][0] > time:
+            return aux(i, mid)
+        elif dfv.value[mid][0] < time:
+            return aux(mid, j)
+        else:
+            return dfm.value[mid]
+
+    return aux(0, len(dfm.value)-1)
+
+def increment_reserv_bucket(bucket, ceils, reserv, last_term_by_id):
+    idx = 0
+    while idx < len(ceils) and ceils[idx] < reserv:
+        idx += 1
+
+    bucket[idx] += 1
+
+def for_each_joined(x):
+    task_id = x[0]
+    ts = x[1]
+
+    term = -1
+    ts = sorted(ts, key=lambda x: x.time)
+
+    request_ceils = [0.025, 0.05, 0.075]
+    cpu_request = [0] * 4 # [a, b, c, d] where <0.025, [0.025, 0.05), [0.05,0.075), >=0.075
+    ram_request = [0] * 4 # [a, b, c, d] where <0.025, [0.025, 0.05), [0.05,0.075), >=0.075
+
+    reserv_ceils = [0, 0.2, 0.4, 0.6, 0.8, 1]
+    cpu_reservs = [0] * 7 # [n, a, b, c, d, e, f] where:
+    ram_reservs = [0] * 7
+
+    for i, t in enumerate(ts):
+        machine_log = get_machine_time_resources(mid, t.time)
+        if machine_log is not None:
+            reserv_cpu = tot_req[0] / machine_logs[0][2]
+            reserv_ram = tot_req[1] / machine_logs[0][3]
+        else:
+            reserv_cpu = -1
+            reserv_ram = -1
+        # 8a-b
+        increment_reserv_bucket(cpu_request, request_ceils, t.id, t.rcpu)
+        increment_reserv_bucket(ram_request, request_ceils, t.id, t.rram)
+        # 8c-d
+        increment_reserv_bucket(cpu_reservs, reserv_ceils, t.id, reserv_cpu)
+        increment_reserv_bucket(ram_reservs, reserv_ceils, t.id, reserv_ram)
+
+        if t.type >= 4 and t.type <= 8:
+            term = t.type
+
+    res = {-1: None, 4: None, 5: None, 6: None, 7: None, 8: None}
+    res[term] = {'rcpu': cpu_request, 'rram': ram_request, 'rscpu': cpu_reservs, 'rsram': ram_reservs}
+
+    return res
+
+def fold_resobjs(ro1, ro2):
+    if ro1 is None:
+        return ro2
+    elif ro2 is None:
+        return ro1
+    else:
+        for k in ro1.keys():
+            for kk in ro1[k].keys():
+                if ro1[k][kk] is None:
+                    ro1[k][kk] = ro2[k][kk]
+                elif ro2[k][kk] is None:
+                    continue
+                else:
+                    ro1[k][kk] = [sum(x) for x in zip(ro1[k][kk], ro2[k][kk])]
+        return ro1
+
+result = df.rdd \
+    .groupBy(lambda x: x.id) \
+    .map(for_each_joined) \
+    .fold(None, fold_resobjs)
+
+d = os.path.dirname(os.path.realpath(__file__))
+
+with open(d + "/" + cluster + "_figure8abcd.json", "w") as f:
+    json.dump(result, f)
+
+# vim: set ts=4 sw=4 et tw=120: