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diff --git a/conclusion.tex b/conclusion.tex
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+\begin{frame}{Conclusion}
+\begin{itemize}
+    \item Kubespray takes longer to instantiate new function instances.
+    \item K3s and MicroK8s, removing unnecessary components, have reduced the deployment time and complexity, improving performance for the majority of serverless edge workloads. \pause \textbf{\alert{We can say that they are comparable.}}
+\end{itemize}
+\end{frame}
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diff --git a/content.tex b/content.tex
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+\begin{frame}{Content}
+
+\begin{enumerate}
+    \item<1-> What is Kubernetes?
+    \item<2-> What is Serverless?
+    \item<3-> How can we combine both?
+    \item<4-> Which K8s distribution is more efficient for serveless development?
+\end{enumerate}
+
+\end{frame}
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diff --git a/k8s.tex b/k8s.tex
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+% -------- Frame 1 ------
+\begin{frame}{Kubernetes}
+\begin{figure}
+    \centering
+    \includegraphics[width=0.2\linewidth]{static/Kubernetes_logo_without_workmark.svg.png}
+\end{figure}
+ The development was started by Google in 2014, but is now developed by Cloud Native Computing Foundation. 
+It is the most widely used container orchestrator.
+
+\end{frame}
+
+% -------- Frame 2 ------
+\begin{frame}{Kubernetes: Architecture}
+
+\begin{figure}
+    \centering
+    \includegraphics[width=1\linewidth]{static/Untitled-2023-09-27-1503(3).png}
+\end{figure}
+
+\begin{itemize}
+    \item<2-> Something a bit less complex?
+\end{itemize}
+
+\end{frame}
+
+% -------- Frame 3 ------
+\begin{frame}{Kubernetes: Distributions}
+
+There are a lot of distributions of K8s such as:
+
+\begin{itemize}
+    \item Kubespray \uncover<2->{\alert{\textit{It uses a set of Ansible playbooks}}}
+    \item K3s \uncover<3->{\alert{\textit{Lightweight packaged as a single binary}}}
+    \item MicroK8s \uncover<4->{\alert{\textit{It works on any GNU/Linux distributions using Snap package manager}}}
+\end{itemize}
+
+\end{frame}
diff --git a/main.tex b/main.tex
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+\documentclass{beamer}
+\usepackage{tikz}
+\usetheme{Copenhagen}
+\usecolortheme{beaver}
+
+\setbeamertemplate{footline}
+{
+  \leavevmode%
+  \hbox{%
+  \begin{beamercolorbox}[wd=.9\paperwidth,ht=2.25ex,dp=1ex,center]{author in head/foot}%
+  \end{beamercolorbox}%
+  \begin{beamercolorbox}[wd=.1\paperwidth,ht=2.25ex,dp=1ex,center]{title in head/foot}%
+    \insertframenumber{} / \inserttotalframenumber
+  \end{beamercolorbox}}%
+  \vskip0pt%
+}
+
+\title[Kubernetes distributions for the edge: serverless performance evaluation]{Kubernetes distributions for the edge: serverless performance evaluation \footnotesize{[1]}}
+\author[]{Santo Cariotti}
+
+\date[]{University of Bologna, 2024-06-17}
+
+\begin{document}
+\frame{\titlepage}
+
+\input{content}
+\input{k8s}
+\input{serverless}
+\input{tests}
+\input{conclusion}
+
+
+\input{refs}
+
+
+\end{document}
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diff --git a/refs.tex b/refs.tex
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+\begin{frame}{References}
+
+\begin{itemize}
+    \item {[1]} Kjorveziroski, V. and Filiposka, S., 2022. Kubernetes distributions for the edge: serverless performance evaluation. The Journal of Supercomputing, 78(11), pp.13728-13755.
+    \item {[2]} Kjorveziroski, V., Bernad Canto, C., Juan Roig, P., Gilly, K., Mishev, A., Trajkovikj, V. and Filiposka, S., 2021. IoT serverless computing at the edge: Open issues and research direction. Transactions on Networks and Communications.
+    \item {[3]} https://prometheus.io
+    \item {[4]} Kim, J. and Lee, K., 2019, July. Functionbench: A suite of workloads for serverless cloud function service. In 2019 IEEE 12th International Conference on Cloud Computing (CLOUD) (pp. 502-504). IEEE.
+
+\end{itemize}
+
+\end{frame}
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diff --git a/serverless.tex b/serverless.tex
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+% -------------------- frame 1 -------------------
+\begin{frame}{Serverless}
+
+\begin{itemize}
+\item<1-> Serverless computing abstracts the underlying infrastructure, focusing solely on the logic that needs to be performed to solve a given task.
+
+\item<2-> A developer just write a function using their favourite programming language and put it online.
+
+\item<3-> A new concept of Function-as-a-Service.
+
+\item<4-> Edge computing is recommended by setting up compute infrastructure closer to the data source.
+
+\item<5-> It is a new frontier for IoT computing [2].
+
+\end{itemize}
+    
+\end{frame}
+
+% ----------------- frame 2 ----------------
+
+\begin{frame}{Serverless architecture}
+    \begin{figure}
+        \centering
+        \includegraphics[width=1\linewidth]{static/Untitled-2023-09-27-1503(4).png}
+    \end{figure}
+\end{frame}
+
+% ------------------ frame 3 ---------------------
+\begin{frame}{Serverless platforms}
+
+There is a new market for serverless platforms, both open and closed source.
+
+\begin{itemize}
+    \item AWS Lambda
+    \item OpenWhisk
+    \item Kubeless
+    \item Knative
+    \item OpenFaaS
+\end{itemize}
+    
+\end{frame}
+
+% ------------------ frame 3 ---------------------
+\begin{frame}{Serverless platforms}
+
+There is a new market for serverless platforms, both open and closed source.
+
+\begin{itemize}
+    \item AWS Lambda
+    \item OpenWhisk
+    \item Kubeless
+    \item Knative
+    \item OpenFaaS \alert{\textit{we chose this one!}}
+\end{itemize}
+    
+\end{frame}
+
+% ---------------- frame 4 ----------------------
+\begin{frame}{OpenFaaS}
+
+Its architecture is composed by:
+
+\begin{itemize}
+    \item API Gateway
+    \item Prometheus [3]
+    \item Watchdog
+    \item Docker Swarm or Kubernetes
+    \item Docker
+\end{itemize}
+
+It supports two different function scaling modes:
+
+\begin{itemize}
+    \item Native scaling based on internal customized metrics
+    \item Kubernetes Horizontal Pod Autoscaler (HPA)
+\end{itemize}
+
+\end{frame}
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diff --git a/static/11227_2022_4430_Fig2_HTML.jpg b/static/11227_2022_4430_Fig2_HTML.jpg
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diff --git a/static/11227_2022_4430_Fig5_HTML.jpg b/static/11227_2022_4430_Fig5_HTML.jpg
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diff --git a/static/11227_2022_4430_Fig6_HTML.jpg b/static/11227_2022_4430_Fig6_HTML.jpg
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diff --git a/static/11227_2022_4430_Fig8_HTML.jpg b/static/11227_2022_4430_Fig8_HTML.jpg
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diff --git a/static/Kubernetes_logo_without_workmark.svg.png b/static/Kubernetes_logo_without_workmark.svg.png
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diff --git a/static/Untitled-2023-09-27-1503(3).png b/static/Untitled-2023-09-27-1503(3).png
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diff --git a/static/Untitled-2023-09-27-1503(4).png b/static/Untitled-2023-09-27-1503(4).png
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diff --git a/tests.tex b/tests.tex
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+% -- frame 1---
+\begin{frame}{Set up the testing machine}
+\begin{itemize}
+    \item Ubuntu 20.04
+    \item CPU Intel Xeon X5647
+    \item 8 GB RAM
+    \item 1 Gbps
+    \item Kubernetes 1.20.7 and 1.23.0
+    \item OpenFaaS 0.21.1
+    \item 1 master node
+    \item Calico as CNI
+    \item Longhorn for persistent volumes
+    \item Docker Engine as CRI \pause \textit{\alert{dockershim is no longer supported after 1.24}}
+    
+\end{itemize}
+    
+\end{frame}
+
+% -- frame 2--
+\begin{frame}{Choose the testing functions}
+Let's choose 14 tests from the FunctionBench serverless benchmarking suite [4].
+
+\begin{itemize}
+    \item 8 from CPU \& Memory category.
+    \item 4 from Disk I/O category.
+    \item 2 from Network performance category.
+\end{itemize}
+    
+\end{frame}
+
+% --- frame 3 ---
+\begin{frame}{How do we run?}
+
+5 tests for each Kubernetes distribution. Executed using the recommended OpenFaaS of-watchdog template for Python 3.7.
+
+\end{frame}
+
+% -- frame 4 --
+\begin{frame}{Cold start performance}
+Each function is executed 100 times to test the cold start delay. \pause
+After every execution, the number of instances for the function is scaled to 0. A new container instance needs to be created before a response is returned.
+\end{frame}
+
+% -- frame 5 --
+\begin{frame}{Cold start performance — Results}
+Kubespray exhibits a 15\% increase compared to both K3s and MicroK8s.
+
+\begin{figure}
+    \includegraphics[width=0.5\linewidth]{static/11227_2022_4430_Fig1_HTML.jpg}\hfill
+    \includegraphics[width=0.5\linewidth]{static/11227_2022_4430_Fig2_HTML.jpg}
+\end{figure}
+
+\end{frame}
+
+% -- frame 6 --
+\begin{frame}{Cold start performance — Results}
+Is the performance difference between K3s and MicroK8s statistically significant?
+\pause
+
+Using a Mann-Whitney U test with \(\alpha = 0.05\) and 
+\begin{itemize}
+    \item H0: the two populations are equal
+    \item H0: the two populations are not equal
+\end{itemize}
+
+we have a \(p\)-value = 0.202, so we can't reject the null hypothesis.
+
+\end{frame}
+
+% -- frame 7 --
+\begin{frame}{Serial execution performance}
+Each function is continuously invoked for a period of 5 min using a single thread. Once a response is received, a new request is immediately sent. Auto-scaling is manually disabled.
+\end{frame}
+
+% -- frame 8 --
+\begin{frame}{Serial execution performance — Results}
+Once again, Kubespray results are slower than both K3s and MicroK8s.
+
+\begin{figure}
+    \centering
+    \includegraphics[width=0.6\linewidth]{static/11227_2022_4430_Fig5_HTML.jpg}
+\end{figure}
+\end{frame}
+
+% -- frame 9 --
+\begin{frame}{Serial execution performance — Results}
+Is the performance difference between K3s and MicroK8s statistically significant?
+\pause
+
+Using a Kruskal-Wallis test with \(\alpha = 0.05\) and 
+\begin{itemize}
+    \item H0: the population medians are equal
+    \item H0: the population medians are not equal
+\end{itemize}
+
+where the null hypothesis failed to be rejected for the video-processing test. Keeping the same hypothesis we can perform the Mann-Whitney U test where, this time, the null hypothesis is rejected in 10 of the 14 tests. The null hypothesis can't be rejected for 3 CPU and 1 network benchmarks.
+\end{frame}
+
+% -- frame 10 --
+\begin{frame}{Parallel execution performance using a single replica}
+Each function is invoked for a fixed amount of time using varying concurrency to determine the performance of the auto-scaling behavior. \textit{Reduced isolation.}
+\end{frame}
+
+
+% -- frame 11 --
+\begin{frame}{Parallel execution performance using a single replica — Results}
+This time Kubespray has better performance than both K3s and MicroK8s for 6 of the 14 tests.
+
+\begin{figure}
+    \centering
+    \includegraphics[width=0.5\linewidth]{static/11227_2022_4430_Fig6_HTML.jpg}
+\end{figure}
+\end{frame}
+
+% -- frame 12 --
+\begin{frame}{Parallel execution using native OpenFaaS auto scaling}
+Each function is invoked for a fixed amount of time using varying concurrency to determine the performance of the auto-scaling behavior.
+\pause
+It tests the number of successful invocations per second for the last 10 seconds: if the number is larger than 5, it scales up the number of function instances up to a preconfigured maximum.
+\end{frame}
+
+
+% -- frame 13 --
+\begin{frame}{Parallel execution using native OpenFaaS auto scaling  — Results}
+Performances are tests by 1 request per second from 6 concurrent workers for more than 200 seconds, successfully reaching the defined threshold for the maximum number of replicas.
+The current number of deployed replicas are not taken but this leads to suboptimal scaling decision scaling to maximum number of configured replicas or not scaling at all under a consistent load.
+\end{frame}
+
+
+% -- frame 14 --
+\begin{frame}{Parallel execution using Kubernetes Horizonal Pod Autoscaler}
+Each function is invoked as the same way as the previous test using the Kubernetes native mechanism. For this test, HPA is configured with a profile which is fired whenever the float-operation function has used more than 350 CPU shares (0.35 of a core). \pause
+We find out results for three different execution strategy:
+
+\begin{itemize}
+    \item Start from 1 replica, execute 2 concurrent req/s, increasing the concurrency rate by 2 every 5 min, until 48 req/s are achieved.
+    \item Start from 1 replica, execute 40 concurrent req/s, decreasing the concurrency rate by 2 every 5 min, until 2 req/s are achieved.
+    \item Start from 1 replica and vary the number of concurrent requests every 5 min using the strategy 8, 1, 20, 4, 40, 24, 1, 4, 16, 1, 36, 32.
+\end{itemize}
+\end{frame}
+
+
+% -- frame 15 --
+\begin{frame}{Parallel execution using Kubernetes Horizonal Pod Autoscaler  — Results}
+Kubespray exhibits higher response times across the three tests, while the results obtained from K3S and MicroK8s are similar.
+
+\begin{figure}
+    \centering
+    \includegraphics[width=1\linewidth]{static/11227_2022_4430_Fig8_HTML.jpg}
+\end{figure}
+\end{frame}