network traffic paul german, jeffrey klow, and emily andrulis
TRANSCRIPT
NETWORK TRAFFICPaul German, Jeffrey Klow, and Emily Andrulis
THE IDEA
Ben’s proposal Examine Cornell’s Network Traffic How much do we use? When do we use it? What information can we glean?
OUR MAIN QUESTIONS
What does an average day at Cornell look like in regards to network traffic?
Assuming the pattern holds, at what point
should we consider getting more bandwidth because we will be frequently coming close to our maximum allotted?
GETTING THE DATA
The Tims in Network Services Log data files for primary and secondary
internet provider, and internal network traffic Log files include upload and download
averages and maximums Decreasing time resolution between lines
Solution: Collect data for 1 week around same time each day
DATA CLEANING
Create scripts in R log file -> data frames in R Update already made data frames with new log data
Add different time variables UNIX -> CST, date, time, weekday, decimal time
Add % of bandwidth variables Helper functions
getSelectedIndices modifyDataResolution
TELLING THE STORY
Use static, animated, and interactive graphs to display data
Go back to our focus questions: Average day at Cornell? Frequency of reaching 85% bandwidth? What does the future usage look like?
EXPLAINING THREE TYPES
Log files from primary internet provider, secondary internet provider, and internal network traffic
Cap differences: 300 Mb/sec vs. 100 Mb/sec Internal weird
AVERAGE USAGE SECONDARY
AVERAGES THROUGH ANIMATION
Day of Week compared to Average Day
AVERAGE LAST WEEK
Average Day compared to Days Last Week
AVERAGES SINCE NOVEMBER
Average Day compared to all days back to November
AVERAGE BLOCK USAGE
Showing Usage over Block 4
BLOCK 4 SECONDARY
Block 4 Usage on Secondary Provider (Note: peaks)
WHERE ARE WE HEADING?
FUTURE APPLICATIONS
Give code to the Tims Documented and split up by task
Interactive graphs with new data Easily replicable Raise awareness about usage in terms of
averages and when we’re nearing the cap