1. NETWORK TRAFFIC Paul German, Jeffrey Klow, and Emily Andrulis

2. 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?

3. 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?

4. 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

5. 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

6. 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?

7. 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

8. AVERAGE USAGE AT CORNELL  Static -> InteractiveInteractive

9. AVERAGE USAGE SECONDARY

10. AVERAGES THROUGH ANIMATION  Day of Week compared to Average Day

11. AVERAGE LAST WEEK  Average Day compared to Days Last Week

12. AVERAGES SINCE NOVEMBER  Average Day compared to all days back to November

13. AVERAGE BLOCK USAGE  Showing Usage over Block 4

14. BLOCK 4 SECONDARY  Block 4 Usage on Secondary Provider (Note: peaks)

15. WHERE ARE WE HEADING?

16. FUTURE APPLICATIONS  Give code to the Tims  Documented and split up by task  Interactive graphs with new data Interactive graphs  Easily replicable  Raise awareness about usage in terms of averages and when we’re nearing the cap