bachelorThesis/report/Claudio_Maggioni_report.tex

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\title{Understanding and Comparing Unsuccessful Executions in Large Datacenters}
%\subtitle{The (optional) subtitle}
\author{Claudio Maggioni}
\versiondate{\today}

\begin{committee}
\advisor[Universit\`a della Svizzera Italiana,
Switzerland]{Prof.}{Walter}{Binder}
\assistant[Universit\`a della Svizzera Italiana,
Switzerland]{Dr.}{Andrea}{Ros\'a}
\end{committee}

\abstract{The project aims at comparing two different traces coming from large
datacenters, focusing in particular on unsuccessful executions of jobs and
tasks submitted by users. The objective of this project is to compare the
resource waste caused by unsuccessful executions, their impact on application
performance, and their root causes. We will show the strong negative impact on
CPU and RAM usage and on task slowdown.  We will analyze patterns of
unsuccessful jobs and tasks, particularly focusing on their interdependency.
Moreover, we will uncover their root causes by inspecting key workload and
system attributes such asmachine locality and concurrency level.}

\begin{document}
\maketitle
\tableofcontents
\newpage

\section{Introduction}
In today's world there is an ever growing demand for efficient, large scale
computations. The rising trend of ``big data'' put the need for efficient
management of large scaled parallelized computing at an all time high. This fact
also increases the demand for research in the field of distributed systems, in
particular in how to schedule computations effectively, avoid wasting resources
and avoid failures.

In 2011 Google released a month long data trace of its own \textit{Borg} cluster
management system\cite{google-marso-11}, containing a lot of data regarding
scheduling, priority management, and failures of a real production workload.
This data was the foundation of the 2015 Ros\'a et al.\ paper
\textit{Understanding the Dark Side of Big Data Clusters: An Analysis beyond
Failures}\cite{vino-paper}, which in its many conclusions highlighted the need
for better cluster management highlighting the high amount of failures found in
the traces.

In 2019 Google released an updated version of the \textit{Borg} cluster
traces\cite{google-marso-19},
not only containing data from a far bigger workload due to the sheer power of
Moore's law, but also providing data from 8 different \textit{Borg} cells from
datacenters all over the world. These new traces are therefore about 100 times
larger than the old traces, weighing in terms of storage spaces approximately
8TiB (when compressed and stored in JSONL format)\cite{google-drive-marso},
requiring considerable computational power to analyze them and the
implementation of special data engineering tecniques for analysis of the data.

This project aims to repeat the analysis performed in 2015 to highlight
similarities and differences in workload this decade brought, and expanding the
old analysis to understand even better the causes of failures and how to prevent
them. Additionally, this report will provide an overview on the data engineering
tecniques used to perform the queries and analyses on the 2019 traces.

\section{Background information}

\hypertarget{introduction}{%
\subsection{Introduction}\label{introduction}}

\textbf{TBD}

\hypertarget{rosuxe0-et-al.-2015-dsn-paper}{%
\subsection{Rosà et al.~2015 DSN
paper}\label{rosuxe0-et-al.-2015-dsn-paper}}

In 2015, Dr.~Andrea Rosà, Lydia Y. Chen, Prof.~Walter Binder published a
research paper titled \textit{Understanding the Dark Side of Big Data
Clusters: An Analysis beyond Failures}\cite{vino-paper} performing several analysis on
Google's 2011 Borg cluster traces. The salient conclusion of that
research is that lots of computation performed by Google would
eventually fail, leading to large amounts of computational power being
wasted.

Our aim with this thesis is to repeat the analysis performed in 2015 on
the new 2019 dataset to find similarities and differences with the
previous analysis, and ulimately find if computational power is indeed
wasted in this new workload as well.

\hypertarget{google-borg}{%
\subsection{Google Borg}\label{google-borg}}

Borg is Google's own cluster management software. Among the various
cluster management services it provides, the main ones are: job queuing,
scheduling, allocation, and deallocation due to higher priority
computations.

The data this thesis is based on is from 8 Borg ``cells''
(i.e.~clusters) spanning 8 different datacenters, all focused on
``compute'' (i.e.~computational oriented) workloads. The data collection
timespan matches the entire month of May 2019.

In Google's lingo a ``job'' is a large unit of computational workload
made up of several ``tasks'', i.e.~a number of executions of single
executables running on a single machine. A job may run tasks
sequentially or in parallel, and the condition for a job's succesful
termination is nontrivial.

Both tasks and jobs lifecyles are represented by several events, which
are encoded and stored in the trace as rows of various tables. Among the
information events provide, the field ``type'' provides information on
the execution status of the job or task. This field can have several values,
which are illustrated in figure~\ref{fig:eventtypes}.

\begin{figure}[h]
\begin{center}
\begin{tabular}{p{3cm}p{12cm}}
\toprule
\textbf{Type code} & \textbf{Description} \\
\midrule
	\texttt{QUEUE} & The job or task was marked not eligible for scheduling
  by Borg's scheduler, and thus Borg will move the job/task in a long
  wait queue\\
\texttt{SUBMIT}  & The job or task was submitted to Borg for execution\\
\texttt{ENABLE}  & The job or task became eligible for scheduling\\
\texttt{SCHEDULE}  & The job or task's execution started\\
\texttt{EVICT}  & The job or task was terminated in order to free
   computational resources for an higher priority job\\
\texttt{FAIL}  & The job or task terminated its execution unsuccesfully
   due to a failure\\
\texttt{FINISH}  & The job or task terminated succesfully\\
\texttt{KILL}  & The job or task terminated its execution because of a
   manual request to stop it\\
\texttt{LOST}  & It is assumed a job or task is has been terminated, but
   due to missing data there is insufficent information to identify when
   or how\\
\texttt{UPDATE\_PENDING}  & The metadata (scheduling class, resource
   requirements, \ldots) of the job/task was updated while the job was
   waiting to be scheduled\\
\texttt{UPDATE\_RUNNING}  & The metadata (scheduling class, resource
   requirements, \ldots) of the job/task was updated while the job was in
   execution\\
\bottomrule
\end{tabular}
\end{center}
  \caption{Overview of job and task event types.}\label{fig:eventtypes}
\end{figure}

Figure~\ref{fig:eventTypes} shows the expected transitions between event
types.

\begin{figure}[h]
\centering
	\resizebox{\textwidth}{!}{%
		\includegraphics{./figures/event_types.png}}
\caption{Typical transitions between task/job event types according to
Google\label{fig:eventTypes}}
\end{figure}

\hypertarget{traces-contents}{%
\subsection{Traces contents}\label{traces-contents}}

The traces provided by Google contain mainly a collection of job and
task events spanning a month of execution of the 8 different clusters.
In addition to this data, some additional data on the machines'
configuration in terms of resources (i.e.~amount of CPU and RAM) and
additional machine-related metadata.

Due to Google's policy, most identification related data (like job/task
IDs, raw resource amounts and other text values) were obfuscated prior
to the release of the traces. One obfuscation that is noteworthy in the
scope of this thesis is related to CPU and RAM amounts, which are
expressed respetively in NCUs (\emph{Normalized Compute Units}) and NMUs
(\emph{Normalized Memory Units}).

NCUs and NMUs are defined based on the raw machine resource
distributions of the machines within the 8 clusters. A machine having 1
NCU CPU power and 1 NMU memory size has the maximum amount of raw CPU
power and raw RAM size found in the clusters. While RAM size is measured
in bytes for normalization purposes, CPU power was measured in GCU
(\emph{Google Compute Units}), a proprietary CPU power measurement unit
used by Google that combines several parameters like number of
processors and cores, clock frequency, and architecture (i.e.~ISA).

\hypertarget{overview-of-traces-format}{%
\subsection{Overview of traces'
format}\label{overview-of-traces-format}}

The traces have a collective size of approximately 8TiB and are stored
in a Gzip-compressed JSONL (JSON lines) format, which means that each
table is represented by a single logical ``file'' (stored in several
file segments) where each carriage return separated line represents a
single record for that table.

There are namely 5 different table ``files'':
\begin{description}
\item[\texttt{machine\_configs},] which is a table containing each physical
  machine's configuration and its evolution over time;
\item[\texttt{instance\_events},] which is a table of task events;
\item[\texttt{collection\_events},] which is a table of job events;
\item[\texttt{machine\_attributes},] which is a table containing (obfuscated)
  metadata about each physical machine and its evolution over time;
\item[\texttt{instance\_usage},] which contains resource (CPU/RAM) measures
  of jobs and tasks running on the single machines.
\end{description}

The scope of this thesis focuses on the tables
\texttt{machine\_configs}, \texttt{instance\_events} and
\texttt{collection\_events}.

\hypertarget{remark-on-traces-size}{%
\subsection{Remark on traces size}\label{remark-on-traces-size}}

While the 2011 Google Borg traces were relatively small, with a total
size in the order of the tens of gigabytes, the 2019 traces are quite
challenging to analyze due to their sheer size. As stated before, the
traces have a total size of 8 TiB when stored in the format provided by
Google. Even when broken down to table ``files'', unitary sizes still
reach the single tebibyte mark (namely for \texttt{machine\_configs},
the largest table in the trace).

Due to this constraints, a careful data engineering based approach was
used when reproducing the 2015 DSN paper analysis. Bleeding edge data
science technologies like Apache Spark were used to achieve efficient
and parallelized computations. This approach is discussed with further
detail in the following section.

\hypertarget{project-requirements-and-analysis}{%
\section{Project requirements and
analysis}\label{project-requirements-and-analysis}}

\textbf{TBD} (describe our objective with this analysis in detail)

\hypertarget{analysis-methodology}{%
\section{Analysis methodology}\label{analysis-methodology}}

Due to the inherent complexity in analyzing traces of this size, novel
bleeding-edge data engineering tecniques were adopted to performed the required
computations. We used the framework Apache Spark to perform efficient and
parallel Map-Reduce computations. In this section, we discuss the technical
details behind our approach.

\hypertarget{introduction-on-apache-spark}{%
\subsection{Introduction on Apache
Spark}\label{introduction-on-apache-spark}}

Apache Spark is a unified analytics engine for large-scale data
processing. In layman's terms, Spark is really useful to parallelize
computations in a fast and streamlined way.

In the scope of this thesis, Spark was used essentially as a Map-Reduce
framework for computing aggregated results on the various tables. Due to
the sharded nature of table ``files'', Spark is able to spawn a thread
per file and run computations using all processors on the server
machines used to run the analysis.

Spark is also quite powerful since it provides automated thread pooling
services, and it is able to efficiently store and cache intermediate
computation on secondary storage without any additional effort required
from the data engineer. This feature was especially useful due to the
sheer size of the analyzed data, since the computations required to
store up to 1TiB of intermediate data on disk.

The chosen programming language for writing analysis scripts was Python.
Spark has very powerful native Python bindings in the form of the
\emph{PySpark} API, which were used to implement the various queries.

\hypertarget{query-architecture}{%
\subsection{Query architecture}\label{query-architecture}}

\subsubsection{Overview}

In general, each query written to execute the analysis
follows a general Map-Reduce template.

Traces are first read, then parsed, and then filtered by performing selections,
projections and computing new derived fields. After this preparation phase, the
trace records are often passed through a \texttt{groupby()} operation, which by
choosing one or many record fields sorts all the records into several ``bins''
containing records with matching values for the selected fields. Then, a map
operation is applied to each bin in order to derive some aggregated property
value for each grouping. Finally, a reduce operation is applied to either
further aggregate those computed properties or to generate an aggregated data
structure for storage purposes.

\subsubsection{Parsing table files}

As stated before, table ``files'' are composed of several Gzip-compressed
shards of JSONL record data.  The specification for the types and constraints
of each record is outlined by Google in the form of a protobuffer specification
file found in the trace release
package\cite{google-proto-marso}. This file was used as
the oracle specification and was a critical reference for writing the query
code that checks, parses and carefully sanitizes the various JSONL records
prior to actual computations.

The JSONL encoding of traces records is often performed with non-trivial rules
that required careful attention. One of these involved fields that have a
logically-wise ``zero'' value (i.e.~values like ``0'' or the empty string). For
these values the key-value pair in the JSON object is outright omitted. When
reading the traces in Apache Spark is therefore necessary to check for this
possibility and insert back the omitted record attributes.

\subsubsection{The queries}

Most queries use only two or three fields in each trace records, while the
original table records often are made of a couple of dozen fields. In order to save
memory during the query, a projection is often applied to the data by the means
of a \texttt{.map()} operation over the entire trace set, performed using
Spark's RDD API.

Another operation that is often necessary to perform prior to the Map-Reduce
core of each query is a record filtering process, which is often motivated by
the presence of incomplete data (i.e.~records which contain fields whose values
is unknown). This filtering is performed using the \texttt{.filter()} operation
of Spark's RDD API.

The core of each query is often a \texttt{groupby()} followed by a
\texttt{map()} operation on the aggregated data. The \texttt{groupby()} groups
the set of all records into several subsets of records each having something in
common. Then, each of this small clusters is reduced with a \texttt{map()}
operation to a single record. The motivation behind this way of computing data
is that for the analysis in this thesis it is often necessary to analyze the
behaviour w.r.t. time of either task or jobs by looking at their events. These
queries are therefore implemented by \texttt{groupby()}-ing records by task or
job, and then \texttt{map()}-ing each set of event records sorting them by time
and performing the desired computation on the obtained chronological event log.

Sometimes intermediate results are saved in Spark's parquet format in order to
compute and save intermediate results beforehand.

\subsection{Query script design}

In this section we aim to show the general complexity behind the implementations
of query scripts by explaining in detail some sampled scripts to better
appreciate their behaviour.

\subsubsection{The ``task slowdown'' query script}

One example of analysis script with average complexity and a pretty
straightforward structure is the pair of scripts \texttt{task\_slowdown.py} and
\texttt{task\_slowdown\_table.py} used to compute the ``task slowdown'' tables
(namely the tables in figure~\ref{fig:taskslowdown}).

``Slowdown'' is a task-wise measure of wasted execution time for tasks with a
\texttt{FINISH} termination type. It is computed as the total execution time of
the task divided by the execution time actually needed to complete the task
(i.e. the total time of the last execution attempt, successful by definition).

The analysis requires to compute the mean task slowdown for each task priority
value, and additionally compute the percentage of tasks with successful
terminations per priority. The query therefore needs to compute the execution
time of each execution attempt for each task, determine if each task has
successful termination or not, and finally combine this data to compute
slowdown, mean slowdown and ultimately the final table found in
figure~\ref{fig:taskslowdown}.

\begin{figure}[h]
\centering
\includegraphics[width=.75\textwidth]{figures/task_slowdown_query.png}
\caption{Diagram of the script used for the ``task slowdown''
  query.}\label{fig:taskslowdownquery}
\end{figure}

Figure~\ref{fig:taskslowdownquery} shows a schematic representation of the query
structure.

The query first starts reading the \texttt{instance\_events} table, which
contains (among other data) all task event logs containing properties, event
types and timestamps. As already explained in the previous section, the logical
table file is actually stored as several Gzip-compressed JSONL shards. This is
very useful for processing purposes, since Spark is able to parse and load in
memory each shard in parallel, i.e. using all processing cores on the server
used to run the queries.

After loading the data, a selection and a projection operation are performed in
the preparation phase so as to ``clean up'' the records and fields that are not
needed, leaving only useful information to feed in the ``group by'' phase. In
this query, the selection phase removes all records that do not represent task
events or that contain an unknown task ID or a null event timestamp. In the 2019
traces it is quite common to find incomplete records, since the log process is
unable to capture the sheer amount of events generated by all jobs in a exact
and deterministic fashion.

Then, after the preparation stage is complete, the task event records are
grouped in several bins, one per task ID\@. Performing this operation the
collection of unsorted task event types is rearranged to form groups of task
events all relating to a single task.

These obtained collections of task events are then sorted by timestamp and
processed to compute intermediate data relating to execution attempt times and
task termination counts. After the task events are sorted, the script iterates
over the events in chronological order, storing each execution attempt time and
registering all execution termination types by checking the event type field.
The task termination is then equal to the last execution termination type,
following the definition originally given in the 2015 Ros\'a et al. DSN paper.

If the task termination is determined to be unsuccessful, the tally counter of
task terminations for the matching task property is increased. Otherwise, all
the task termination attempt time deltas are returned. Tallies and time deltas
are saved in an intermediate time file for fine-grained processing.

Finally, the \texttt{task\_slowdown\_table.py} processes this intermediate
results to compute the percentage of successful tasks per execution and
computing slowdown values given the previously computed execution attempt time
deltas. Finally, the mean of the computed slowdown values is computed resulting
in the clear and coincise tables found in figure~\ref{fig:taskslowdown}.


\hypertarget{ad-hoc-presentation-of-some-analysis-scripts}{%
\subsection{Ad-Hoc presentation of some analysis
scripts}\label{ad-hoc-presentation-of-some-analysis-scripts}}

\textbf{TBD} (with diagrams)

\hypertarget{analysis-and-observations}{%
\section{Analysis and observations}\label{analysis-and-observations}}

\hypertarget{overview-of-machine-configurations-in-each-cluster}{%
\subsection{Overview of machine configurations in each
cluster}\label{overview-of-machine-configurations-in-each-cluster}}

\input{figures/machine_configs}

Refer to figure \ref{fig:machineconfigs}.

\textbf{Observations}:

\begin{itemize}
\item
  machine configurations are definitely more varied than the ones in the
  2011 traces
\item
  some clusters have more machine variability
\end{itemize}

\hypertarget{analysis-of-execution-time-per-each-execution-phase}{%
\subsection{Analysis of execution time per each execution
phase}\label{analysis-of-execution-time-per-each-execution-phase}}

\input{figures/machine_time_waste}

Refer to figures \ref{fig:machinetimewaste-abs} and
\ref{fig:machinetimewaste-rel}.

\textbf{Observations}:

\begin{itemize}
\item
  Across all cluster almost 50\% of time is spent in ``unknown''
  transitions, i.e. there are some time slices that are related to a
  state transition that Google says are not ``typical'' transitions.
  This is mostly due to the trace log being intermittent when recording
  all state transitions.
\item
  80\% of the time spent in KILL and LOST is unknown. This is
  predictable, since both states indicate that the job execution is not
  stable (in particular LOST is used when the state logging itself is
  unstable)
\item
  From the absolute graph we see that the time ``wasted'' on non-finish
  terminated jobs is very significant
\item
  Execution is the most significant task phase, followed by queuing time
  and scheduling time (``ready'' state)
\item
  In the absolute graph we see that a significant amount of time is
  spent to re-schedule evicted jobs (``evicted'' state)
\item
  Cluster A has unusually high queuing times
\end{itemize}

\hypertarget{task-slowdown}{%
\subsection{Task slowdown}\label{task-slowdown}}

\input{figures/task_slowdown}

Refer to figure \ref{fig:taskslowdown}

\textbf{Observations}:

\begin{itemize}
\item
  Priority values are different from 0-11 values in the 2011 traces. A
  conversion table is provided by Google;
\item
  For some priorities (e.g.~101 for cluster D) the relative number of
  finishing task is very low and the mean slowdown is very high (315).
  This behaviour differs from the relatively homogeneous values from the
  2011 traces.
\item
  Some slowdown values cannot be computed since either some tasks have a
  0ns execution time or for some priorities no tasks in the traces
  terminate successfully. More raw data on those exception is in
  Jupyter.
\item
  The \% of finishing jobs is relatively low comparing with the 2011
  traces.
\end{itemize}

\hypertarget{reserved-and-actual-resource-usage-of-tasks}{%
\subsection{Reserved and actual resource usage of
tasks}\label{reserved-and-actual-resource-usage-of-tasks}}

\input{figures/spatial_resource_waste}

Refer to figures \ref{fig:spatialresourcewaste-actual} and
\ref{fig:spatialresourcewaste-requested}.

\textbf{Observations}:

\begin{itemize}
\item
  Most (mesasured and requested) resources are used by killed job, even
  more than in the 2011 traces.
\item
  Behaviour is rather homogeneous across datacenters, with the exception
  of cluster G where a lot of LOST-terminated tasks acquired 70\% of
  both CPU and RAM
\end{itemize}

\hypertarget{correlation-between-task-events-metadata-and-task-termination}{%
\subsection{Correlation between task events' metadata and task
termination}\label{correlation-between-task-events-metadata-and-task-termination}}

\input{figures/figure_7}

Refer to figures \ref{fig:figureVII-a}, \ref{fig:figureVII-b}, and
\ref{fig:figureVII-c}.

\textbf{Observations}:

\begin{itemize}
\item
  No smooth curves in this figure either, unlike 2011 traces
\item
  The behaviour of curves for 7a (priority) is almost the opposite of
  2011, i.e. in-between priorities have higher kill rates while
  priorities at the extremum have lower kill rates. This could also be
  due bt the inherent distribution of job terminations;
\item
  Event execution time curves are quite different than 2011, here it
  seems there is a good correlation between short task execution times
  and finish event rates, instead of the U shape curve in 2015 DSN
\item
  In figure \ref{fig:figureVII-b} cluster behaviour seems quite uniform
\item
  Machine concurrency seems to play little role in the event termination
  distribution, as for all concurrency factors the kill rate is at 90\%.
\end{itemize}

\hypertarget{correlation-between-task-events-resource-metadata-and-task-termination}{%
\subsection{Correlation between task events' resource metadata and task
termination}\label{correlation-between-task-events-resource-metadata-and-task-termination}}

\hypertarget{correlation-between-job-events-metadata-and-job-termination}{%
\subsection{Correlation between job events' metadata and job
termination}\label{correlation-between-job-events-metadata-and-job-termination}}

\input{figures/figure_9}

Refer to figures \ref{fig:figureIX-a}, \ref{fig:figureIX-b}, and
\ref{fig:figureIX-c}.

\textbf{Observations}:

\begin{itemize}
\item
  Behaviour between cluster varies a lot
\item
  There are no ``smooth'' gradients in the various curves unlike in the
  2011 traces
\item
  Killed jobs have higher event rates in general, and overall dominate
  all event rates measures
\item
  There still seems to be a correlation between short execution job
  times and successfull final termination, and likewise for kills and
  higher job terminations
\item
  Across all clusters, a machine locality factor of 1 seems to lead to
  the highest success event rate
\end{itemize}

\hypertarget{mean-number-of-tasks-and-event-distribution-per-task-type}{%
\subsection{Mean number of tasks and event distribution per task
type}\label{mean-number-of-tasks-and-event-distribution-per-task-type}}

\input{figures/table_iii}

Refer to figure \ref{fig:tableIII}.

\textbf{Observations}:

\begin{itemize}
\item
  The mean number of events per task is an order of magnitude higher
  than in the 2011 traces
\item
  Generally speaking, the event type with higher mean is the termination
  event for the task
\item
  The \# evts mean is higher than the sum of all other event type means,
  since it appears there are a lot more non-termination events in the
  2019 traces.
\end{itemize}

\hypertarget{mean-number-of-tasks-and-event-distribution-per-job-type}{%
\subsection{Mean number of tasks and event distribution per job
type}\label{mean-number-of-tasks-and-event-distribution-per-job-type}}

\input{figures/table_iv}

Refer to figure \ref{fig:tableIV}.

\textbf{Observations}:

\begin{itemize}
\item
  Again the mean number of tasks is significantly higher than the 2011
  traces, indicating a higher complexity of workloads
\item
  Cluster A has no evicted jobs
\item
  The number of events is however lower than the event means in the 2011
  traces
\end{itemize}

\hypertarget{probability-of-task-successful-termination-given-its-unsuccesful-events}{%
\subsection{Probability of task successful termination given its
unsuccesful
events}\label{probability-of-task-successful-termination-given-its-unsuccesful-events}}

\input{figures/figure_5}

Refer to figure \ref{fig:figureV}.

\textbf{Observations}:

\begin{itemize}
\item
  Behaviour is very different from cluster to cluster
\item
  There is no easy conclusion, unlike in 2011, on the correlation
  between succesful probability and \# of events of a specific type.
\item
  Clusters B, C and D in particular have very unsmooth lines that vary a
  lot for small \# evts differences. This may be due to an uneven
  distribution of \# evts in the traces.
\end{itemize}

\hypertarget{potential-causes-of-unsuccesful-executions}{%
\subsection{Potential causes of unsuccesful
executions}\label{potential-causes-of-unsuccesful-executions}}

\textbf{TBD}

\hypertarget{implementation-issues-analysis-limitations}{%
\section{Implementation issues -- Analysis
limitations}\label{implementation-issues-analysis-limitations}}

\hypertarget{discussion-on-unknown-fields}{%
\subsection{Discussion on unknown
fields}\label{discussion-on-unknown-fields}}

\textbf{TBD}

\hypertarget{limitation-on-computation-resources-required-for-the-analysis}{%
\subsection{Limitation on computation resources required for the
analysis}\label{limitation-on-computation-resources-required-for-the-analysis}}

\textbf{TBD}

\hypertarget{other-limitations}{%
\subsection{Other limitations \ldots{}}\label{other-limitations}}

\textbf{TBD}

\hypertarget{conclusions-and-future-work-or-possible-developments}{%
\section{Conclusions and future work or possible
developments}\label{conclusions-and-future-work-or-possible-developments}}

\textbf{TBD}

\printbibliography

\end{document}
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