From b74cf8ee26ed5a07dd415b3e7f6730502979cb7c Mon Sep 17 00:00:00 2001
From: noah <noah@theschricks.com>
Date: Thu, 6 Apr 2023 16:09:18 -0500
Subject: [PATCH] Database Load

---
 Schrick-Noah_MPI-Tasking.aux |  40 +++++++++++----------
 Schrick-Noah_MPI-Tasking.bbl |   5 +++
 Schrick-Noah_MPI-Tasking.blg |  54 ++++++++++++++--------------
 Schrick-Noah_MPI-Tasking.log |  68 ++++++++++++++++++-----------------
 Schrick-Noah_MPI-Tasking.pdf | Bin 1005307 -> 1008332 bytes
 Schrick-Noah_MPI-Tasking.tex |  18 +++++++++-
 6 files changed, 107 insertions(+), 78 deletions(-)

diff --git a/Schrick-Noah_MPI-Tasking.aux b/Schrick-Noah_MPI-Tasking.aux
index ea06aa6..2023bd9 100644
--- a/Schrick-Noah_MPI-Tasking.aux
+++ b/Schrick-Noah_MPI-Tasking.aux
@@ -85,33 +85,28 @@
 \newlabel{table:tasking-gen-perf}{{II}{5}{Task Descriptions and Performance Notes}{table.2}{}}
 \@writefile{toc}{\contentsline {subsection}{\numberline {\mbox  {VI-A}}Number of Exploits}{5}{subsection.6.1}\protected@file@percent }
 \@writefile{toc}{\contentsline {subsection}{\numberline {\mbox  {VI-B}}Applicability of Exploits}{5}{subsection.6.2}\protected@file@percent }
-\citation{Slurm}
+\citation{cook_rage_2018}
+\citation{li_concurrency_2019}
+\citation{li_combining_2019}
 \@writefile{lof}{\contentsline {figure}{\numberline {6}{\ignorespaces Example of a Not Applicable Exploit for the MPI Tasking Testing}}{6}{figure.6}\protected@file@percent }
 \newlabel{fig:NA-exp}{{6}{6}{Example of a Not Applicable Exploit for the MPI Tasking Testing}{figure.6}{}}
 \@writefile{lof}{\contentsline {figure}{\numberline {7}{\ignorespaces Example of an Artificially Applicable Exploit for the MPI Tasking Testing}}{6}{figure.7}\protected@file@percent }
 \newlabel{fig:Appl-exp}{{7}{6}{Example of an Artificially Applicable Exploit for the MPI Tasking Testing}{figure.7}{}}
 \@writefile{toc}{\contentsline {subsection}{\numberline {\mbox  {VI-C}}Database Load}{6}{subsection.6.3}\protected@file@percent }
-\@writefile{toc}{\contentsline {subsection}{\numberline {\mbox  {VI-D}}Testing Platform}{6}{subsection.6.4}\protected@file@percent }
-\@writefile{toc}{\contentsline {subsection}{\numberline {\mbox  {VI-E}}Testing Process}{6}{subsection.6.5}\protected@file@percent }
-\@writefile{toc}{\contentsline {section}{\numberline {VII}Results}{6}{section.7}\protected@file@percent }
-\newlabel{sec:Tasking-Results}{{VII}{6}{Results}{section.7}{}}
+\citation{Slurm}
 \bibdata{Bibliography}
 \bibcite{9678822}{1}
 \bibcite{7993827}{2}
-\bibcite{8652334}{3}
-\bibcite{baloyi_guidelines_2019}{4}
-\bibcite{allman_complying_2006}{5}
+\@writefile{toc}{\contentsline {subsection}{\numberline {\mbox  {VI-D}}Testing Platform}{7}{subsection.6.4}\protected@file@percent }
+\@writefile{toc}{\contentsline {subsection}{\numberline {\mbox  {VI-E}}Testing Process}{7}{subsection.6.5}\protected@file@percent }
+\@writefile{toc}{\contentsline {section}{\numberline {VII}Results}{7}{section.7}\protected@file@percent }
+\newlabel{sec:Tasking-Results}{{VII}{7}{Results}{section.7}{}}
 \@writefile{toc}{\contentsline {section}{\numberline {VIII}Analysis}{7}{section.8}\protected@file@percent }
 \@writefile{toc}{\contentsline {section}{\numberline {IX}Conclusion}{7}{section.9}\protected@file@percent }
 \@writefile{toc}{\contentsline {section}{References}{7}{section*.1}\protected@file@percent }
-\@writefile{lof}{\contentsline {figure}{\numberline {8}{\ignorespaces Speedup and Efficiency of the MPI Tasking Approach for a Varying Number of Compute Nodes with an Increasing Problem Size}}{7}{figure.8}\protected@file@percent }
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-\newlabel{fig:Tasking-Spd}{{10}{7}{Results for the MPI Tasking Approach in Terms of Speedup}{figure.10}{}}
-\@writefile{lof}{\contentsline {figure}{\numberline {11}{\ignorespaces Results for the MPI Tasking Approach in Terms of Efficiency}}{7}{figure.11}\protected@file@percent }
-\newlabel{fig:Tasking-Eff}{{11}{7}{Results for the MPI Tasking Approach in Terms of Efficiency}{figure.11}{}}
+\bibcite{8652334}{3}
+\bibcite{baloyi_guidelines_2019}{4}
+\bibcite{allman_complying_2006}{5}
 \bibcite{j_hale_compliance_nodate}{6}
 \bibcite{ou_scalable_2006}{7}
 \bibcite{CPSIOT}{8}
@@ -131,7 +126,16 @@
 \bibcite{cook_scalable_2016}{22}
 \bibcite{li_concurrency_2019}{23}
 \bibcite{9150145}{24}
+\@writefile{lof}{\contentsline {figure}{\numberline {8}{\ignorespaces Speedup and Efficiency of the MPI Tasking Approach for a Varying Number of Compute Nodes with an Increasing Problem Size}}{8}{figure.8}\protected@file@percent }
+\newlabel{fig:Spd-Eff-Task}{{8}{8}{Speedup and Efficiency of the MPI Tasking Approach for a Varying Number of Compute Nodes with an Increasing Problem Size}{figure.8}{}}
+\@writefile{lof}{\contentsline {figure}{\numberline {9}{\ignorespaces MPI Tasking Approach Runtime Results}}{8}{figure.9}\protected@file@percent }
+\newlabel{fig:Tasking-RT}{{9}{8}{MPI Tasking Approach Runtime Results}{figure.9}{}}
+\@writefile{lof}{\contentsline {figure}{\numberline {10}{\ignorespaces Results for the MPI Tasking Approach in Terms of Speedup}}{8}{figure.10}\protected@file@percent }
+\newlabel{fig:Tasking-Spd}{{10}{8}{Results for the MPI Tasking Approach in Terms of Speedup}{figure.10}{}}
+\@writefile{lof}{\contentsline {figure}{\numberline {11}{\ignorespaces Results for the MPI Tasking Approach in Terms of Efficiency}}{8}{figure.11}\protected@file@percent }
+\newlabel{fig:Tasking-Eff}{{11}{8}{Results for the MPI Tasking Approach in Terms of Efficiency}{figure.11}{}}
 \bibcite{7087377}{25}
-\bibcite{Slurm}{26}
+\bibcite{li_combining_2019}{26}
+\bibcite{Slurm}{27}
 \bibstyle{ieeetr}
-\gdef \@abspage@last{8}
+\gdef \@abspage@last{9}
diff --git a/Schrick-Noah_MPI-Tasking.bbl b/Schrick-Noah_MPI-Tasking.bbl
index 0c5e4d8..4942d70 100644
--- a/Schrick-Noah_MPI-Tasking.bbl
+++ b/Schrick-Noah_MPI-Tasking.bbl
@@ -132,6 +132,11 @@ K.~Kaynar and F.~Sivrikaya, ``Distributed attack graph generation,'' {\em IEEE
   Transactions on Dependable and Secure Computing}, vol.~13, no.~5,
   pp.~519--532, 2016.
 
+\bibitem{li_combining_2019}
+M.~Li, P.~Hawrylak, and J.~Hale, ``Combining {OpenCL} and {MPI} to support
+  heterogeneous computing on a cluster,'' {\em ACM International Conference
+  Proceeding Series}, 2019.
+
 \bibitem{Slurm}
 SchedMD, ``Slurm {Workload} {Manager}.''
 
diff --git a/Schrick-Noah_MPI-Tasking.blg b/Schrick-Noah_MPI-Tasking.blg
index b102127..cb50ce4 100644
--- a/Schrick-Noah_MPI-Tasking.blg
+++ b/Schrick-Noah_MPI-Tasking.blg
@@ -5,45 +5,45 @@ The style file: ieeetr.bst
 Database file #1: Bibliography.bib
 Warning--entry type for "Slurm" isn't style-file defined
 --line 1112 of file Bibliography.bib
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+cite$ -- 27
+duplicate$ -- 359
+empty$ -- 693
+format.name$ -- 78
+if$ -- 1648
 int.to.chr$ -- 0
-int.to.str$ -- 26
-missing$ -- 22
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-num.names$ -- 25
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+int.to.str$ -- 27
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+while$ -- 60
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diff --git a/Schrick-Noah_MPI-Tasking.log b/Schrick-Noah_MPI-Tasking.log
index 012ab24..7103371 100644
--- a/Schrick-Noah_MPI-Tasking.log
+++ b/Schrick-Noah_MPI-Tasking.log
@@ -1,4 +1,4 @@
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+
+Underfull \hbox (badness 7451) in paragraph at lines 293--294
 \OT1/ptm/m/n/10 All nodes are connected with a 10Gbps Infiniband
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+
+
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 []\OT1/ptm/m/n/10 In terms of efficiency, 2 compute nodes offer the
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 \OT1/ptm/m/n/8 A Focus on Cyberphysical Systems and Internet of Things,'' in
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+
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 []\OT1/ptm/m/n/8 P. Pacheco, \OT1/ptm/m/it/8 An Introduction to Parallel Progra
 mming\OT1/ptm/m/n/8 .  Morgan
@@ -688,7 +691,8 @@ Underfull \hbox (badness 10000) in paragraph at lines 110--114
 \OT1/ptm/m/n/8 ``The Boost Graph Library, vers. 1.75.0.''
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 ** Conference Paper **
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diff --git a/Schrick-Noah_MPI-Tasking.tex b/Schrick-Noah_MPI-Tasking.tex
index 86fc977..c863cb3 100644
--- a/Schrick-Noah_MPI-Tasking.tex
+++ b/Schrick-Noah_MPI-Tasking.tex
@@ -264,6 +264,22 @@ The applicability of exploits was tested by using percentages of overall exploit
 \end{figure}
 
 \subsection{Database Load}
+The database load parameter is a parameter passed to RAGE to determine when to offload data. The generation of large-scale attack and compliance graphs often faces challenges with scalability and state space explosion. For these large-scale graphs, as the generation process progresses, the resulting graph and the queue of unexplored states begins to consume too much memory for most systems, and the process either needs to offload the data or run the risk of its process being killed for constraints on memory consumption. Works by the authors of \cite{cook_rage_2018}, \cite{li_concurrency_2019}, and \cite{li_combining_2019} strive for maximum performance of the generation process, which involves making full use of system memory. Since network operations, database operations, and reading and writing from disks slows the generation, it is often preferred to store all data in memory. As a result, there is a balance between performance and system memory consumption. 
+
+RAGE has the option for automatically offloading to a PostgreSQL database based on its memory consumption. The database load parameter can either be a float between 0 and 1, or can be an integer greater than 1. If the parameter is a float, RAGE will automatically offload its graph instance or frontier if the memory consumed by either exceeds a percentage of total system memory corresponding to the float value. If the parameter is an integer greater than 1, RAGE wil automatically offload its graph instance or frontier if the number of items in either is greater than the parameter value.
+
+Since the goal of the stress tests is to generate identical graphs for all tests while still stressing each task, the load parameter was carefully altered. The total number of states generated by RAGE is known in advance since the resulting graph has already been generated, and will be constant for all stress tests. For the automobile example being tested, the total number of states is 394. In order to test the database load, it is preferable to use an integer value for the load parameter rather than a float. By using an integer value, it is possible to specifically target how often the offloading process should occur. If a float value was used, additional work would be needed to give RAGE only a certain amount of system memory per test. Though possible, there is more simplicity with passing in static integer values since the graph is known in advance.
+
+The database load parameter was changed based on percentage of the total resulting graph size, as follows:
+\begin{itemize}
+    \item{0\% Load (Do not ever write to the database) - DBLoad = 395}
+    \item{25\% Load (Write to the database when 25\% of the total resulting graph size is in memory) - DBLoad = 79}
+    \item{50\% Load (Write to the database when 50\% of the total resulting graph size is in memory) - DBLoad = 197}
+    \item{75\% Load (Write to the database when 75\% of the total resulting graph size is in memory) - DBLoad = 296}
+    \item{100\% Load (Write to the database on every new state) - DBLoad = 1}
+\end{itemize}
+
+The database load parameter stresses Tasks 0, 4, and 5. Task 4 will be stressed on all load parameters, except for when the load is 0\% (size 395), which serves as the control. Task 4 will experience the greatest stress when the load parameter is 100\% (size 1), since as soon as new states are discovered in previous tasks, Task 4 will begin. Task 0 and Task 5 will experience stress at the same intervals. When the queue of unexplored states increases to a size greater than the load parameter, Task 5 will empty the queue, and Task 0 will be forced to pull new states from the database.
 
 \subsection{Testing Platform}
 All data was collected on a 13 node cluster, with 12 nodes serving as dedicated compute nodes, and 1 node serving as the login node. Each compute node has a configuration as follows:
@@ -277,7 +293,7 @@ All data was collected on a 13 node cluster, with 12 nodes serving as dedicated
 All nodes are connected with a 10Gbps Infiniband interconnect.
 
 \subsection{Testing Process}
-Each parameter discussed in this section was individually changed until all permutations of parameters were explored. In addition to changing the parameters, all tests were conducted on a varying number of nodes. All permutations of parameters were examined on 1 compute node (serially) through 12 compute nodes. A bash script for looping through parameters was created on the distributed computing testing platform, with jobs sent to Slurm Workload Manager \cite{Slurm}. When a job completed with Slurm, the bash script would use grep on the output file to extract the necessary data, and add it to a CSV file that was used for the data analysis.
+Each parameter discussed in this section was individually changed until all permutations of parameters were explored. In addition to changing the parameters, all tests were conducted on a varying number of nodes. All permutations of parameters were examined on 1 compute node (serially) through 12 compute nodes. A bash script for looping through parameters was created on the distributed computing testing platform, with jobs sent to Slurm Workload Manager \cite{Slurm}. When a job is completed with Slurm, the bash script would use grep on the output file to extract the necessary data, and add it to a CSV file that was used for the data analysis.
 
 \section{Results} \label{sec:Tasking-Results}
 A series of tests were conducted on the platform described at the beginning of Section \ref{sec:test-platform}, and results were collected in regards to the effect of the MPI Tasking approach on increasing sizes of exploit lists for a varying number of nodes. The exploit list initially began with 6 items, and each test scaled the number of exploits by a factor of 2. The final test was with an exploit list with 49,512 entries. If all of the items in these exploit lists were applicable, the runtime would be too great for feasible testing due to the state space explosion. To prevent state-space explosion but still gather valid results, each exploit list in the tests contained 6 exploits that could be applicable, and all remaining exploits were not applicable. The not applicable exploits were created in a fashion similar to that seen in Figure \ref{fig:NA-exp}. By creating a multitude of not applicable exploits, testing can safely be conducted by ensuring state space explosion would not occur while still observing the effectiveness of the tasking approach.