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@@ -167,6 +167,7 @@
 
    VOLTHA system tests <voltha-system-tests/README.md>
    testing/index.rst
+   testing/memory_usage_pprof.rst
 
 .. toctree::
    :maxdepth: 1
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+Memory Usage investigation
+==========================
+
+This page shows how to analyze the memory consumption of a VOLTHA container (written in Go) by using a
+custom built image of the container and the ``pprof`` tool, provided by the GO Language.
+
+
+Build an instrumented docker image
+----------------------------------
+
+If the Makefile and the Helm Charts of your image support building and deploying profiled images (should be the case),
+you can create a profiled image, via the ``make docker-build`` target adding the option ``BUILD_PROFILED=true``.
+The command will generate two images- the normal and the profiled one
+Following is an example for the ``voltha-openonu-adapter-go``, please customize the values according to your
+docker registry and desired tag:
+
+.. code:: bash
+
+  DOCKER_REGISTRY="hhildebr/" DOCKER_TAG="hh-dev" BUILD_PROFILED=true  make docker-build
+
+.. code:: bash
+
+  bbsim@bbsim:~$ docker images
+  REPOSITORY                          TAG             IMAGE ID      CREATED      SIZE
+  hhildebr/voltha-openonu-adapter-go  hh-dev-profile  e142bba3afea  2 hours ago  725MB
+  hhildebr/voltha-openonu-adapter-go  hh-dev          8b6c5436a97d  2 hours ago  37.8MB
+
+Deploy the instrumented image
+-----------------------------
+
+Now you can deploy VOLTHA by using the custom ``<image_name>-profile`` via helm flag.
+Also the profiler flag must be activated.
+Following is a snippet to put into a ``values.yaml`` file.
+
+.. code:: bash
+
+ voltha-adapter-openonu:
+  images:
+    adapter_open_onu_go:
+    repository: hhildebr/voltha-openonu-adapter-go
+    tag: hh-dev-profile
+    pullPolicy: "Never"
+  profiler:
+    enabled: true
+
+Verify and expose the profiler endpoint
+---------------------------------------
+
+To verify that the associated profiler server was started successfully and the endpoint is exposed in kubernetes,
+use the following command:
+
+.. code:: bash
+
+  kubectl get svc --all-namespaces
+
+The expected output is:
+
+.. code:: bash
+
+  NAMESPACE   NAME                                   TYPE      CLUSTER-IP EXTERNAL-IP PORT(S)  AGE
+  …
+  voltha      voltha-voltha-adapter-openonu-profiler ClusterIP None       <none>      6060/TCP 2h
+  …
+
+To then be able to communicate with the profile server from your host system, you have to configure an
+appropriate port forwarding. An example is the following:
+
+.. code:: bash
+
+  kubectl -n voltha port-forward --address 0.0.0.0 svc/voltha1-voltha-adapter-openonu-profiler 6060:6060
+
+Use the pprof tool
+------------------
+
+Static Mode
+^^^^^^^^^^^
+
+The profiling tool can be now interacted with and data retrieved.
+Following is an example of how to get the heap usage of the container and save it to a file:
+
+.. code:: bash
+
+  curl http://127.0.0.1:6060/debug/pprof/heap > onu-go-heap.pprof
+
+To get a first idea of the system's status, a graphical overview of the heap usage can be created from the extracted
+data using the ``pprof`` tool and displayed in the browser.
+
+.. code:: bash
+
+  go tool pprof -http=:8080  onu-go-heap.pprof
+
+In order to save the graphical representation of the data permanently, an image file can be created from it
+(PDF-format is also supported):
+
+.. code:: bash
+
+  go tool pprof -png  onu-go-heap.pprof
+
+Following is an example with heap data collected after hundreds of cycles of startup and deletion
+of multiple BBSIM ONUs. The individual objects/functions can be seen there in relation to each other.
+Already by the size of the boxes you can recognize the objects/functions where to find the memory leaks.
+
+.. figure:: ../_static/1_graph_with_mem_leak.png
+   :alt: Graphical representation of memory usage
+   :width: 70%
+   :align: center
+
+   Graphical representation of memory usage
+
+Interactive Mode
+^^^^^^^^^^^^^^^^
+
+To start a detailed investigation, ``pprof`` is invoked in interactive mode.
+Since pprof assumes by default that the code belonging to the UUT can be found in the /go/src directory,
+this path part must be removed using the trim_path option and replaced with the actual directory part using
+the source_path option.
+Following is an example for the openonu adapter:
+
+.. code:: bash
+
+  go tool pprof  -source_path /home/bbsim/temp_pperf/voltha-openonu-adapter-go -trim_path /go/src /mnt/shared/onu-go-heap.pprof
+
+by using the ``top`` command one can see which are the main originators of memory leaks,
+observing the the first ten objects/functions displayed:
+
+.. figure:: ../_static/2_top_with_mem_leak.png
+   :alt: Ten highest memory functions
+   :width: 80%
+   :align: center
+
+   Ten highest memory functions
+
+If more object/functions are to be displayed, simply add the desired number to the command - e.g. ``top100``.
+
+
+The next step is to use the ``list <function_name>`` command to view the detailed memory allocations.
+
+Example Memory leak analysis and fix
+------------------------------------
+Following is an example applied for the number one of the top10, function ``NewOnuDeviceEntry``.
+
+.. figure:: ../_static/3_list_with_mem_leak.png
+   :alt: List function details before optimization
+   :width: 80%
+   :align: center
+
+   List function details before optimization
+
+Since the single allocation of ``onuDeviceEntry.omciRebootMessageReceivedChannel`` should occupy at most 100kB of
+heap memory (2048 standard OMCI messages), we can conclude that the 85.90MB of occupied memory represents more
+than 850 instances of ``onuDeviceEntry`` not deleted by the GC.
+
+After removing the cause of the memory leak the same test with hundreds of cycles of startup and deletion of multiple
+BBSIM ONUs shows that the issue has been solved. Having a look at the retrieved pprof data shows a significant
+decrease of memory consumption, about 10MB vs 744MB without the patch:
+
+.. figure:: ../_static/4_top_without_mem_leak.png
+   :alt: List function details after optimization
+   :width: 70%
+   :align: center
+
+   List function details after optimization