diff --git a/scripts/sizing.py b/scripts/sizing.py
new file mode 100644
index 0000000..c976c15
--- /dev/null
+++ b/scripts/sizing.py
@@ -0,0 +1,358 @@
+# Copyright 2017-present Open Networking Foundation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# This tool collects CPU and Memory informations for each container in the VOLTHA stack
+
+# NOTE
+# Collecting the info for all containers in the same chart can be confusing,
+# we may want to create subcharts for the different groups, eg: infra, ONOS, core, adapters
+
+import csv
+from sys import platform as sys_pf
+
+if sys_pf == 'darwin':
+    import matplotlib
+
+    matplotlib.use("TkAgg")
+
+import argparse
+import requests
+import matplotlib.pyplot as plt
+import matplotlib.dates as mdates
+from datetime import datetime
+import time
+
+EXCLUDED_POD_NAMES = [
+    "kube", "coredns", "kind", "grafana",
+    "prometheus", "tiller", "control-plane",
+    "calico", "nginx", "registry", "cattle", "canal", "metrics",
+]
+
+DATE_FORMATTER_FN = mdates.DateFormatter('%Y-%m-%d %H:%M:%S')
+
+KAFKA_TOPICS = [
+    "openolt",
+    "brcm_openomci_onu",
+    "voltha",
+    "adapters",
+    "rwcore"
+]
+
+def main(address, out_folder, since, namespace="default"):
+    """
+    Query Prometheus and generate .pdf files for CPU and Memory consumption for each POD
+    :param address: string The address of the Prometheus instance to query
+    :param out_folder: string The output folder (where to save the .pdf files)
+    :param since: int When to start collection data (minutes in the past)
+    :return: void
+    """
+    time_delta = int(since) * 60
+
+    container_mem_query = "sum by(pod) (container_memory_working_set_bytes{namespace='%s',container!='',container!='POD'})" % namespace
+
+    container_cpu_query = "sum by(pod) (rate(container_cpu_usage_seconds_total{namespace='%s',container!='',container!='POD'}[%sm])) * 100" % (namespace, since)
+
+    now = time.time()
+    cpu_params = {
+        "query": container_cpu_query,
+        "start": now - time_delta,
+        "end": now,
+        "step": "30",
+    }
+
+    r = requests.get("http://%s/api/v1/query_range" % address, cpu_params)
+    print("Downloading CPU info from: %s" % r.url)
+    container_cpu = r.json()["data"]["result"]
+    containers = remove_unwanted_containers(container_cpu)
+    plot_cpu_consumption(containers,
+                         output="%s/cpu.pdf" % out_folder)
+    data_to_csv(containers, output="%s/cpu.csv" % out_folder,
+                convert_values=lambda values: ["{:.2f}".format(v) for v in values])
+
+    mem_params = {
+        "query": container_mem_query,
+        "start": now - time_delta,
+        "end": now,
+        "step": "30",
+    }
+
+    r = requests.get("http://%s/api/v1/query_range" % address, mem_params)
+    print("Downloading Memory info from: %s" % r.url)
+    container_mem = r.json()["data"]["result"]
+    containers = remove_unwanted_containers(container_mem)
+    plot_memory_consumption(containers, output="%s/memory.pdf" % out_folder)
+    data_to_csv(containers, output="%s/memory.csv" % out_folder,
+                convert_values=lambda values: ["{:.2f}".format(bytesto(v, "m")) for v in values])
+
+    print("Downloading KAFKA stats")
+    get_kafka_stats(address, out_folder)
+    print("Downloading ETCD stats")
+    get_etcd_stats(address, out_folder)
+
+
+
+def data_to_csv(containers, output=None, convert_values=None):
+    """
+    Get a list of prometheus metrics and dumps them in a csv
+    :param containers: Prometheus metrics
+    :param output: Destination file
+    :param convert_values: Function to convert the valus, take a list on numbers
+    """
+    csv_file = open(output, "w+")
+    csv_writer = csv.writer(csv_file, delimiter=',', quotechar='"', quoting=csv.QUOTE_MINIMAL)
+
+    # we assume all the containers have the same timestamps
+    # FIXME pods may have different timestamps depending on when the collection started
+    # - find the longest list in containers
+    # - add empty values at the beginning of the other list
+    dates = [datetime.fromtimestamp(x[0]) for x in containers[0]["values"]]
+    csv_writer.writerow([''] + dates)
+
+    for c in containers:
+        name = c["metric"]["pod"]
+        data = c["values"]
+
+        values = [float(x[1]) for x in data]
+
+        if convert_values:
+            values = convert_values(values)
+        csv_writer.writerow([name] + values)
+
+
+def plot_cpu_consumption(containers, output=None):
+    plt.figure('cpu')
+    fig, ax = plt.subplots()
+    ax.xaxis.set_major_formatter(DATE_FORMATTER_FN)
+    ax.xaxis_date()
+    fig.autofmt_xdate()
+
+    plt.title("CPU Usage per POD")
+    plt.xlabel("Timestamp")
+    plt.ylabel("% used")
+
+    for c in containers:
+        name = c["metric"]["pod"]
+        data = c["values"]
+
+        dates = [datetime.fromtimestamp(x[0]) for x in data]
+
+        values = [float(x[1]) for x in data]
+
+        plt.plot(dates, values, label=name, lw=2, color=get_line_color(name))
+        # plt.plot(dates[1:], get_diff(values), label=name, lw=2, color=get_line_color(name))
+
+    plt.legend(loc='upper left', title="CPU Consumption", bbox_to_anchor=(1.05, 1))
+
+    fig = plt.gcf()
+    fig.set_size_inches(20, 11)
+
+    plt.savefig(output, bbox_inches="tight")
+
+
+def plot_memory_consumption(containers, output=None):
+    plt.figure("memory")
+    fig, ax = plt.subplots()
+    ax.xaxis.set_major_formatter(DATE_FORMATTER_FN)
+    ax.xaxis_date()
+    fig.autofmt_xdate()
+    plt.title("Memory Usage")
+    plt.xlabel("Timestamp")
+    plt.ylabel("MB")
+
+    for c in containers:
+        name = c["metric"]["pod"]
+        data = c["values"]
+
+        dates = [datetime.fromtimestamp(x[0]) for x in data]
+        values = [bytesto(float(x[1]), "m") for x in data]
+
+        # plt.plot(dates[1:], get_diff(values), label=name, lw=2, color=get_line_color(name))
+        plt.plot(dates[1:], values[1:], label=name, lw=2, color=get_line_color(name))
+
+    plt.legend(loc='upper left', title="Memory Usage", bbox_to_anchor=(1.05, 1))
+
+    fig = plt.gcf()
+    fig.set_size_inches(20, 11)
+
+    plt.savefig(output, bbox_inches="tight")
+
+
+def remove_unwanted_containers(cpus):
+    res = []
+    for c in cpus:
+
+        if "pod" in c["metric"]:
+            pod_name = c["metric"]["pod"]
+            if any(x in pod_name for x in EXCLUDED_POD_NAMES):
+                continue
+            res.append(c)
+
+    return res
+
+
+def get_line_color(container_name):
+    colors = {
+        "bbsim0": "#884EA0",
+        "bbsim1": "#9B59B6",
+        "bbsim-sadis-server": "#D2B4DE",
+        "onos-atomix-0": "#85C1E9",
+        "onos-atomix-1": "#7FB3D5",
+        "onos-atomix-2": "#3498DB",
+        "onos-onos-classic-0": "#1A5276",
+        "onos-onos-classic-1": "#1B4F72",
+        "onos-onos-classic-2": "#154360",
+        "etcd-0": "#7D6608",
+        "etcd-1": "#9A7D0A",
+        "etcd-2": "#B7950B",
+        "open-olt-voltha-adapter-openolt": "#7E5109",
+        "open-onu-voltha-adapter-openonu-0": "#6E2C00",
+        "open-onu-voltha-adapter-openonu-1": "#873600",
+        "open-onu-voltha-adapter-openonu-2": "#A04000",
+        "open-onu-voltha-adapter-openonu-3": "#BA4A00",
+        "open-onu-voltha-adapter-openonu-4": "#D35400",
+        "open-onu-voltha-adapter-openonu-5": "#D35400",
+        "open-onu-voltha-adapter-openonu-6": "#E59866",
+        "open-onu-voltha-adapter-openonu-7": "#EDBB99",
+        "kafka-0": "#4D5656",
+        "kafka-1": "#5F6A6A",
+        "kafka-2": "#717D7E",
+        "kafka-zookeeper-0": "#839192",
+        "kafka-zookeeper-1": "#95A5A6",
+        "kafka-zookeeper-2": "#717D7E",
+        "radius": "#82E0AA",
+        "voltha-voltha-ofagent": "#641E16",
+        "voltha-voltha-rw-core": "#7B241C",
+    }
+
+    if container_name in colors:
+        return colors[container_name]
+    elif "openolt" in container_name:
+        return colors["open-olt-voltha-adapter-openolt"]
+    elif "ofagent" in container_name:
+        return colors["voltha-voltha-ofagent"]
+    elif "rw-core" in container_name:
+        return colors["voltha-voltha-rw-core"]
+    elif "bbsim0" in container_name:
+        return colors["bbsim0"]
+    elif "bbsim1" in container_name:
+        return colors["bbsim1"]
+    elif "bbsim-sadis-server" in container_name:
+        return colors["bbsim-sadis-server"]
+    elif "radius" in container_name:
+        return colors["radius"]
+    else:
+        return "black"
+
+
+def get_diff(data):
+    # get the delta between the current data and the previous point
+    return [x - data[i - 1] for i, x in enumerate(data)][1:]
+
+
+def bytesto(b, to, bsize=1024):
+    """convert bytes to megabytes, etc.
+       sample code:
+           print('mb= ' + str(bytesto(314575262000000, 'm')))
+       sample output:
+           mb= 300002347.946
+    """
+
+    a = {'k': 1, 'm': 2, 'g': 3, 't': 4, 'p': 5, 'e': 6}
+    r = float(b)
+    for i in range(a[to]):
+        r = r / bsize
+
+    return r
+
+
+
+def get_etcd_stats(address, out_folder):
+    """
+    :param address: The prometheus address
+    :param out_folder: The folder in which store the output files
+    """
+
+    etcd_stats = {
+        "size":"etcd_debugging_mvcc_db_total_size_in_bytes",
+        "keys":"etcd_debugging_mvcc_keys_total"
+    }
+
+    etcd = {}
+
+    time_delta = 80
+    for  stat,query in etcd_stats.items():
+        now = time.time()
+        etcd_params = {
+            "query": "%s{}" % query,
+            "start": now - time_delta,
+            "end": now,
+            "step": "30",
+        }
+        r = requests.get("http://%s/api/v1/query_range" % address, etcd_params)
+
+        i = r.json()["data"]["result"][0]
+        etcd[stat] = i["values"][-1][1]
+
+    csv_file = open("%s/etcd_stats.csv" % out_folder, "w+")
+    csv_writer = csv.writer(csv_file, delimiter=',', quotechar='"', quoting=csv.QUOTE_MINIMAL)
+
+    for k,v in etcd.items():
+        csv_writer.writerow([k, v])
+
+def get_kafka_stats(address, out_folder):
+    """
+    :param address: The prometheus address
+    :param out_folder: The folder in which store the output files
+    """
+    # get the last information for all topics, we only care about the last value so a short interval is fine
+    now = time.time()
+    time_delta = 80
+    kafka_params = {
+        "query": "kafka_topic_partition_current_offset{}",
+        "start": now - time_delta,
+        "end": now,
+        "step": "30",
+    }
+
+    r = requests.get("http://%s/api/v1/query_range" % address, kafka_params)
+
+    msg_per_topic = {}
+
+    for t  in r.json()["data"]["result"]:
+        # we only care about some topics
+        topic_name = t["metric"]["topic"]
+
+        if any(x in topic_name for x in KAFKA_TOPICS):
+            # get only the value at the last timestamp
+            msg_per_topic[t["metric"]["topic"]] = t["values"][-1][1]
+
+    csv_file = open("%s/kafka_msg_per_topic.csv" % out_folder, "w+")
+    csv_writer = csv.writer(csv_file, delimiter=',', quotechar='"', quoting=csv.QUOTE_MINIMAL)
+
+    for k,v in msg_per_topic.items():
+        csv_writer.writerow([k, v])
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(prog="sizing")
+    parser.add_argument("-a", "--address", help="The address of the Prometheus instance we're targeting",
+                        default="127.0.0.1:31301")
+    parser.add_argument("-o", "--output", help="Where to output the generated files",
+                        default="plots")
+    parser.add_argument("-s", "--since", help="When to start sampling the data (in minutes before now)",
+                        default=10)
+    parser.add_argument("-n", "--namespace", help="Kubernetes namespace for collecting metrics",
+                        default="default")
+
+    args = parser.parse_args()
+    main(args.address, args.output, args.since, args.namespace)