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# Copyright 2017 Open Networking Foundation (ONF)
#
# Please refer questions to either the onos test mailing list at <onos-test@onosproject.org>,
# the System Testing Plans and Results wiki page at <https://wiki.onosproject.org/x/voMg>,
# or the System Testing Guide page at <https://wiki.onosproject.org/x/WYQg>
#
# TestON is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 2 of the License, or
# (at your option) any later version.
#
# TestON is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with TestON. If not, see <http://www.gnu.org/licenses/>.
#
# If you have any questions, or if you don't understand R,
# please contact Jeremy Ronquillo: j_ronquillo@u.pacific.edu
# **********************************************************
# STEP 1: Data management.
# **********************************************************
has_flow_obj = 1
database_host = 2
database_port = 3
database_u_id = 4
database_pw = 5
test_name = 6
branch_name = 7
has_neighbors = 8
old_flow = 9
save_directory = 10
print( "**********************************************************" )
print( "STEP 1: Data management." )
print( "**********************************************************" )
# Command line arguments are read.
print( "Reading commmand-line args." )
args <- commandArgs( trailingOnly=TRUE )
# ----------------
# Import Libraries
# ----------------
print( "Importing libraries." )
library( ggplot2 )
library( reshape2 )
library( RPostgreSQL ) # For databases
# -------------------
# Check CLI Arguments
# -------------------
print( "Verifying CLI args." )
if ( is.na( args[ save_directory ] ) ){
print( paste( "Usage: Rscript SCPFflowTp1g.R",
"<has-flow-obj>",
"<database-host>",
"<database-port>",
"<database-user-id>",
"<database-password>",
"<test-name>",
"<branch-name>",
"<has-neighbors>",
"<using-old-flow>",
"<directory-to-save-graphs>",
sep=" " ) )
quit( status = 1 ) # basically exit(), but in R
}
# -----------------
# Create File Names
# -----------------
print( "Creating filenames and title of graph." )
chartTitle <- "Flow Throughput Test"
fileNeighborsModifier <- "no"
commandNeighborModifier <- ""
fileFlowObjModifier <- ""
sqlFlowObjModifier <- ""
if ( args[ has_flow_obj ] == 'y' ){
fileFlowObjModifier <- "_flowObj"
sqlFlowObjModifier <- "_fobj"
chartTitle <- paste( chartTitle, " with Flow Objectives", sep="" )
}
chartTitle <- paste( chartTitle, "\nNeighbors =", sep="" )
fileOldFlowModifier <- ""
if ( args[ has_neighbors ] == 'y' ){
fileNeighborsModifier <- "all"
commandNeighborModifier <- "scale=1 OR NOT "
chartTitle <- paste( chartTitle, "Cluster Size - 1" )
} else {
chartTitle <- paste( chartTitle, "0" )
}
if ( args[ old_flow ] == 'y' ){
fileOldFlowModifier <- "_OldFlow"
chartTitle <- paste( chartTitle, "With Eventually Consistent Flow Rule Store", sep="\n" )
}
errBarOutputFile <- paste( args[ save_directory ],
args[ test_name ],
"_",
args[ branch_name ],
"_",
fileNeighborsModifier,
"-neighbors",
fileFlowObjModifier,
fileOldFlowModifier,
"_graph.jpg",
sep="" )
# ------------------
# SQL Initialization
# ------------------
print( "Initializing SQL" )
con <- dbConnect( dbDriver( "PostgreSQL" ),
dbname = "onostest",
host = args[ database_host ],
port = strtoi( args[ database_port ] ),
user = args[ database_u_id ],
password = args[ database_pw ] )
# ---------------------------
# Flow Throughput SQL Command
# ---------------------------
print( "Generating Flow Throughput SQL command." )
command <- paste( "SELECT scale, avg( avg ), avg( std ) FROM flow_tp",
sqlFlowObjModifier,
"_tests WHERE (",
commandNeighborModifier,
"neighbors = 0 ) AND branch = '",
args[ branch_name ],
"' AND date IN ( SELECT max( date ) FROM flow_tp",
sqlFlowObjModifier,
"_tests WHERE branch='",
args[ branch_name ],
"' AND ",
( if( args[ old_flow ] == 'y' ) "" else "NOT " ),
"is_old_flow",
" ) GROUP BY scale ORDER BY scale",
sep="" )
print( "Sending SQL command:" )
print( command )
fileData <- dbGetQuery( con, command )
# **********************************************************
# STEP 2: Organize data.
# **********************************************************
print( "**********************************************************" )
print( "STEP 2: Organize Data." )
print( "**********************************************************" )
# ------------
# Data Sorting
# ------------
print( "Sorting data for Flow Throughput." )
colnames( fileData ) <- c( "scale",
"avg",
"std" )
requiredColumns <- c( "avg" )
tryCatch( avgs <- c( fileData[ requiredColumns] ),
error = function( e ) {
print( "[ERROR] One or more expected columns are missing from the data. Please check that the data and SQL command are valid, then try again." )
print( "Required columns: " )
print( requiredColumns )
print( "Actual columns: " )
print( names( fileData ) )
print( "Error dump:" )
print( e )
quit( status = 1 )
}
)
# ----------------------------
# Flow TP Construct Data Frame
# ----------------------------
print( "Constructing Flow TP data frame." )
dataFrame <- melt( avgs ) # This is where reshape2 comes in. Avgs list is converted to data frame
dataFrame$scale <- fileData$scale # Add node scaling to the data frame.
dataFrame$std <- fileData$std
colnames( dataFrame ) <- c( "throughput",
"type",
"scale",
"std" )
dataFrame <- na.omit( dataFrame ) # Omit any data that doesn't exist
print( "Data Frame Results:" )
print( dataFrame )
# **********************************************************
# STEP 3: Generate graphs.
# **********************************************************
print( "**********************************************************" )
print( "STEP 3: Generate Graph." )
print( "**********************************************************" )
# ------------------
# Generate Main Plot
# ------------------
print( "Generating main plot." )
# Create the primary plot here.
# ggplot contains the following arguments:
# - data: the data frame that the graph will be based off of
# - aes: the asthetics of the graph which require:
# - x: x-axis values (usually node scaling)
# - y: y-axis values (usually time in milliseconds)
# - fill: the category of the colored side-by-side bars (usually type)
mainPlot <- ggplot( data = dataFrame, aes( x = scale,
y = throughput,
ymin = throughput,
ymax = throughput + std,
fill = type ) )
# ------------------------------
# Fundamental Variables Assigned
# ------------------------------
print( "Generating fundamental graph data." )
# Formatting the plot
theme_set( theme_grey( base_size = 22 ) ) # set the default text size of the graph.
width <- 0.7 # Width of the bars.
xScaleConfig <- scale_x_continuous( breaks = dataFrame$scale,
label = dataFrame$scale )
xLabel <- xlab( "Scale" )
yLabel <- ylab( "Throughput (,000 Flows/sec)" )
fillLabel <- labs( fill="Type" )
imageWidth <- 15
imageHeight <- 10
imageDPI <- 200
errorBarColor <- rgb( 140, 140, 140, maxColorValue=255 )
theme <- theme( plot.title = element_text( hjust = 0.5,
size = 32,
face = 'bold' ),
plot.subtitle = element_text( size=16, hjust=1.0 ) )
subtitle <- paste( "Last Updated: ", format( Sys.time(), format = "%b %d, %Y at %I:%M %p %Z" ), sep="" )
title <- labs( title = chartTitle, subtitle = subtitle )
# Store plot configurations as 1 variable
fundamentalGraphData <- mainPlot +
xScaleConfig +
xLabel +
yLabel +
fillLabel +
theme +
title
# ---------------------------
# Generating Bar Graph Format
# ---------------------------
# Create the stacked bar graph with error bars.
# geom_bar contains:
# - stat: data formatting (usually "identity")
# - width: the width of the bar types (declared above)
# geom_errorbar contains similar arguments as geom_bar.
print( "Generating bar graph with error bars." )
barGraphFormat <- geom_bar( stat = "identity",
width = width,
fill = "#FFAA3C" )
errorBarFormat <- geom_errorbar( width = width,
position = position_dodge(),
color = errorBarColor )
values <- geom_text( aes( x = dataFrame$scale,
y = dataFrame$throughput + 0.03 * max( dataFrame$throughput ),
label = format( dataFrame$throughput,
digits=3,
big.mark = ",",
scientific = FALSE ) ),
size = 7.0,
fontface = "bold" )
result <- fundamentalGraphData +
barGraphFormat +
errorBarFormat +
values
# -----------------------
# Exporting Graph to File
# -----------------------
print( paste( "Saving bar chart with error bars to", errBarOutputFile ) )
tryCatch( ggsave( errBarOutputFile,
width = imageWidth,
height = imageHeight,
dpi = imageDPI ),
error = function( e ){
print( "[ERROR] There was a problem saving the graph due to a graph formatting exception. Error dump:" )
print( e )
quit( status = 1 )
}
)
print( paste( "[SUCCESS] Successfully wrote bar chart with error bars out to", errBarOutputFile ) )
quit( status = 0 )