diff options
Diffstat (limited to 'sources')
| -rw-r--r-- | sources/dt_features_comparison.tex | 2 | ||||
| -rw-r--r-- | sources/failure_models.tex | 26 | ||||
| -rw-r--r-- | sources/message_broker.drawio | 112 | ||||
| -rwxr-xr-x | sources/novel_eval_method.drawio | 125 | ||||
| -rw-r--r-- | sources/simulator_comparison.tex | 4 | ||||
| -rw-r--r-- | sources/thesis_structure.drawio | 102 |
6 files changed, 336 insertions, 35 deletions
diff --git a/sources/dt_features_comparison.tex b/sources/dt_features_comparison.tex index 788382d..ee849c8 100644 --- a/sources/dt_features_comparison.tex +++ b/sources/dt_features_comparison.tex @@ -21,7 +21,7 @@ \bottomrule \end{tabular} } - \caption{Comparison of selected datacenter digital twins. \textbf{Modelling capability:} {3D = Visualizations}; {CH = Cooling/Heat}, PE = Power/Energy Consumption, A = Anomaly Detection, N = Network Modelling, SE = Scenario Exploration, VP = Virtual Prototyping, FD = Federation, RA = Resource Allocation; \textbf{Data Analytics}: $\ddagger$ = Predictive Analysis; $\star$ = Descriptive Analysis, $\dagger$ = Prescriptive Analysis.} + \caption[Comparison of selected datacenter digital twins.]{Comparison of selected datacenter digital twins. \textbf{Modelling capability:} {3D = Visualizations}; {CH = Cooling/Heat}, PE = Power/Energy Consumption, A = Anomaly Detection, N = Network Modelling, SE = Scenario Exploration, VP = Virtual Prototyping, FD = Federation, RA = Resource Allocation; \textbf{Data Analytics}: $\ddagger$ = Predictive Analysis; $\star$ = Descriptive Analysis, $\dagger$ = Prescriptive Analysis.} \label{tab:dt_features_comparison} \end{table} % Autonomous decisions (autonomous twinning) from~\cite{DBLP:conf/sc/BrewerMKWBHSGGW24} is inherent to digital twinning -- it is better left unsaid in the table. diff --git a/sources/failure_models.tex b/sources/failure_models.tex new file mode 100644 index 0000000..63fa119 --- /dev/null +++ b/sources/failure_models.tex @@ -0,0 +1,26 @@ +\begin{table}[t] + \centering + \renewcommand{\arraystretch}{1.4} + \resizebox{\columnwidth}{!}{ + \begin{tabular}{lllllllll} + \toprule + & \multicolumn{4}{c}{\textbf{Availability intervals}} & \multicolumn{4}{c}{\textbf{Unavailability intervals}} \\ + \cmidrule(lr){2-5} \cmidrule(l){6-9} + \textbf{Trace} & \textbf{Exp($\mu$)} & \textbf{Wbl($k, \lambda$)} & \textbf{LogN($\mu,\sigma$)} & \textbf{Gam($k, \lambda$)} & \textbf{Exp($\mu$)} & \textbf{Wbl($k, \lambda$)} & \textbf{LogN($\mu, \sigma$)} & \textbf{Gam($k, \lambda$)} \\ + lanl05 & 1779.99 & 048 816.60 & 5.56 2.39 & 0.35 5102.71 & 5.92 & 0.58 2.18 & 0.05 1.42 & 0.38 15.44 \\ + g5k06 & 31.41 & 0.48 14.37 & 1.51 2.42 & 0.34 94.35 & 7.41 & 0.35 0.47 & -2.00 2.20 & 0.19 39.92 \\ + microsoft99 & 67.01 & 0.55 35.30 & 2.62 1.84 & 0.41 162.19 & 16.49 & 0.60 9.34 & 1.42 1.54 & 0.46 35.52 \\ + websites02 & 11.85 & 0.46 3.68 & 0.23 2.02 & 0.31 38.67 & 1.18 & 0.65 0.61 & -1.12 1.13 & 0.50 2.37 \\ + pl05 & 159.49 & 0.33 19.35 & 1.44 2.86 & 0.20 788.03 & 49.61 & 0.36 5.59 & 0.40 2.45 & 0.21 237.65 \\ + ldns04 & 141.06 & 0.51 79.30 & 3.25 2.33 & 0.39 362.43 & 8.61 & 0.63 5.62 & 0.91 1.64 & 0.51 16.87 \\ + overnet03 & 2.29 & 0.85 2.04 & 0.19 0.98 & 0.91 2.53 & 12.00 & 0.44 2.98 & 0.08 1.80 & 0.29 41.64 \\ + nd07cpu & 13.73 & 0.45 4.16 & 0.30 2.20 & 0.30 46.16 & 4.25 & 0.51 0.74 & -1.02 1.27 & 0.28 15.07 \\ + skype06 & 16.27 & 0.64 10.86 & 1.60 1.57 & 0.53 30.79 & 14.31 & 0.63 9.48 & 1.40 1.73 & 0.50 28.53 \\ + + \bottomrule + \end{tabular} + } + \caption[The failure models table.]{The failure models table, by Javadi \etal~\cite{DBLP:journals/jpdc/JavadiKIE13}.} + \label{tab:failure_models_table} +\end{table} + diff --git a/sources/message_broker.drawio b/sources/message_broker.drawio new file mode 100644 index 0000000..252677f --- /dev/null +++ b/sources/message_broker.drawio @@ -0,0 +1,112 @@ +<mxfile host="app.diagrams.net"> + <diagram name="Page-1" id="FTKl7TmkcDtvlSIBR7tf"> + <mxGraphModel dx="928" dy="636" grid="1" gridSize="10" guides="1" tooltips="1" connect="1" arrows="1" fold="1" page="1" pageScale="1" pageWidth="850" pageHeight="1100" math="0" shadow="0"> + <root> + <mxCell id="0" /> + <mxCell id="1" parent="0" /> + <mxCell id="WZt3c-7qrFsWFr_eEWth-26" connectable="0" parent="1" style="group" value="" vertex="1"> + <mxGeometry height="270" width="490" x="30" y="20" as="geometry" /> + </mxCell> + <mxCell id="WZt3c-7qrFsWFr_eEWth-27" 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Author: Mastenbroek \etal~\cite{DBLP:conf/ccgrid/MastenbroekAJLB21}} - \label{tab:datacenter_simulator_comparison} + \caption[Comparison of selected datacenter simulators.]{Comparison of selected datacenter simulators. \textbf{Models:} VC = VMs and containers; N = Network, S = Storage, E = Energy, CM = Cost Models, FS = FaaS, ML = Machine Learning, WF = Workflows, FD = Federation; \textbf{Phenomena:} F = Failures, PI = Performance interface; \textbf{Tools:} EXP = Experiment automation; \textbf{Support:} \ding{51} = Yes, \ding{55} = No; $\dagger$ = extension, not integrated; $\star$ = advanced, carefully calibrated feature. Author: Mastenbroek \etal~\cite{DBLP:conf/ccgrid/MastenbroekAJLB21}} + \label{tab:datacenter_simulator_comparison} \end{table} diff --git a/sources/thesis_structure.drawio b/sources/thesis_structure.drawio index 8ef3faa..eef2415 100644 --- a/sources/thesis_structure.drawio +++ b/sources/thesis_structure.drawio @@ -1,33 +1,44 @@ <mxfile host="app.diagrams.net"> <diagram name="Page-1" id="8tJIHZG3YGX61DCj7_IM"> - <mxGraphModel dx="1092" dy="787" grid="1" gridSize="10" guides="1" tooltips="1" connect="1" arrows="1" fold="1" page="1" pageScale="1" pageWidth="850" pageHeight="1100" math="0" shadow="0"> + <mxGraphModel dx="977" dy="669" grid="1" gridSize="10" guides="1" tooltips="1" connect="1" arrows="1" fold="1" page="1" pageScale="1" pageWidth="850" pageHeight="1100" math="0" shadow="0"> <root> <mxCell id="0" /> <mxCell id="1" parent="0" /> + <mxCell id="MXKTANBymBVMColYor_E-4" parent="1" style="rounded=0;whiteSpace=wrap;html=1;fillColor=#f5f5f5;fontColor=#333333;strokeColor=#666666;" 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