1. Realistic and Responsive Network Traffic Generation
April 22, 2019
Realistic and Responsive Network Traffic Generation
Kashi Venkatesh Vishwanath
Amin Vahdat
University of California, San Diego
April 22, 2019

2. Motivation ZCP Evaluation Did we model the right background traffic?
April 22, 2019
Motivation
Background
traffic source
Background
traffic sink
ZCP Evaluation
Home user
Webserver
Did we model the right background traffic?
For 2006, as well as 2010?
Does web traffic affect background traffic?
April 22, 2019

3. Traffic Generation Goals
Realism
Application mix, application characteristics
Average bytes and packet arrival rates
Burstiness of traffic at various timescales
Responsiveness
Competing application can alter background traffic
Ability to project into alternate scenarios
Doubling access link capacity
Changing popularity from HTTP to P2P
April 22, 2019

4. Why Another Traffic Generator?
Existing traffic generators
Aggregate traffic into a single class
Limits ability to vary application mix
Ignore effect of wide-area links on flows
Limits ability to project to alternate scenarios
Cannot reproduce burstiness in traffic
Competing applications could be sensitive to burstiness
No Realistic and Responsive Traffic Generator
April 22, 2019

5. Contributions Swing Realistic and responsive traffic generator
Reproduce observed burstiness in traffic
Identify and examine critical components
Users
Applications
Network
Ability to project traffic into alternate scenarios
Change application mix, user behavior, network characteristics
Details to follow
April 22, 2019

6. Rest of the Talk Traffic Generation Traffic Validation Responsiveness
Is traffic burstiness important?
What is next?
April 22, 2019

7. What Does Traffic Depend On?
Complex interaction at various layers
Users
Periods of activity
Application popularity etc.
Applications
HTTP, SMTP
Request/Response behavior
Network
Packet losses
Latency etc.
Capture and model the interaction between layers
from perspective of a single link
April 22, 2019

8. Methodology Overview Swing trace Generate Swing traffic
Start with a given trace
Program hosts (running real OS, TCP stack) to communicate using the structural model
Extract parameters for users, network, and applications
Model dumb-bell topology
Model target scenario current/alternate
April 22, 2019

9. Modeling Traffic CDFs for model parameters complete
April 22, 2019
Modeling Traffic
Passive TCP trace measurements
Users
Applications
Network
Link capacity
Link latency
Link loss rate
#sessions, intersession
Traffic
SESSION:
#RRE,
interRRE
CDFs for model parameters complete
Next: generate traffic using the CDFs
Classification
Request-Response Exchange (RRE):
#conn,interconn
HTTP SMTP P2P NNTP
(IP,Port)
TCP SEQ, ACK,
timestamps
REQ/RSP sizes numpairs, thinktime
April 22, 2019

10. Traffic Generation Network Emulator 10mbps, 10ms, 2% loss Physical
RSP 1000
RSP 5000
REQ 500
REQ 100
Wait 100ms
Physical
machines
100 mbps,
10ms, 1% loss rate
Record traffic
Hosts
Swing traffic/trace
HTTP SMTP P2P NNTP
Sessions
REQ/RSP,
Numpairs,
thinktime
Connections
RRE
April 22, 2019

11. Results Compare Swing trace to original trace Realism Responsiveness
Coarse-grained behavior e.g., throughput
Modeled parameters e.g., response size
Burstiness of traffic
Responsiveness
What-if scenarios
Is burstiness important?
April 22, 2019

12. Target Traces Mawi 18Mbps CAR ~18Mbps HTTP, SMTP, NAPSTER, NNTP
CAIDA OC48
~200Mbps
HTTP, KAZAA, NNTP
For this talk, AUCK
Auck OC3c ATM
~ 5.5 Mbps
HTTP, SQUID,
SMTP
April 22, 2019
Photo courtesy university of texas library

13. Experimental Setup 10 minute Auck trace 11 Linux + 1 FreeBSD machines
2300 July 24, 2001
11 Linux + 1 FreeBSD machines
Multiplexing 1000 virtual hosts
HTTP, SQUID, and TCPOTHER
Top host – 4% of traffic
Top 100 hosts – 60% of traffic
Top 1000 hosts – 98% of traffic
April 22, 2019

14. Results Compare Swing trace to original trace Realism Responsiveness
Coarse-grained behavior e.g., throughput
Modeled parameters e.g., response size
Burstiness of traffic
Responsiveness
What-if scenarios
Is burstiness important?
April 22, 2019

15. Validation: Burstiness
Coarse-grained behavior insufficient
How do traffic demands peak? At different timescales?
Various techniques:
Index of dispersion of counts/intervals
Power spectral density
Wavelet-based Multi-Resolution Analysis (MRA)
April 22, 2019

16. Energy Plot Example More bursty 1ms 256ms Coarser timescale
April 22, 2019

17. Importance of Network Modeling
More bursty
Users
Application
Network
Capacity
Latency
Loss rates
Coarser timescale
April 22, 2019

18. Importance of Network Modeling
More bursty
Users
Application
Network
Capacity
Latency
Loss rates
Coarser timescale
April 22, 2019

19. Importance of Network Modeling
More bursty
Users
Application
Network
Capacity
Latency
Loss rates
Coarser timescale
April 22, 2019

20. Importance of Network Modeling
More bursty
Users
Application
Network
Capacity
Latency
Loss rates
Important to model network
(capacity, latency, loss rates)
Coarser timescale
April 22, 2019

21. Results Compare Swing trace to original trace Realism Responsiveness
Coarse-grained behavior e.g., throughput
Modeled parameters e.g., response size
Burstiness of traffic
Responsiveness
What-if scenarios
Is burstiness important?
April 22, 2019

22. Results Compare Swing trace to original trace Realism Responsiveness
Coarse-grained behavior e.g., throughput
Modeled parameters e.g., response size
Burstiness of traffic
Responsiveness
What-if scenarios
Is burstiness important?
April 22, 2019

23. Does burstiness matter?
Bittorrent: Sensitivity to Bursty Background Traffic
Does burstiness matter?
Two kinds of background traffic
Both ~ 5.5mbps
Differ in burstiness
100 bittorrent nodes
46MB file download
10mbps, 50ms access links
April 22, 2019

24. Bittorrent: Sensitivity to Bursty Background Traffic
Two kinds of background traffic
Both ~ 5.5mbps
Differ in burstiness
100 bittorrent nodes
46MB file download
10mbps, 50ms access links
Background traffic matters
Burstiness of background traffic matters
April 22, 2019

25. Future Work Improve model
Improve accuracy of model parameter predictions
Single model for all applications
Causality of network events
Reasons for impact of background traffic on end-to-end application behavior
Application sensitivity to specific levels of burstiness
Generalize to larger topologies
April 22, 2019

26. Contributions Realistic and Responsive Traffic Generation
Reproduce burstiness (sub-RTT) for live traffic
Extract and reproduce essential properties
Users, Applications and Network
Impact of burstiness on competing applications
April 22, 2019

27. Questions? More information and code release (soon)
April 22, 2019

28. Extra slides from here on
April 22, 2019

29. Limitations Symmetry of routes Parameters of our model
Network characteristics
Extract all prevailing wide area conditions using a single packet trace
Responsiveness of UDP traffic
April 22, 2019

30. Bidirectional traces
April 22, 2019

31. Sensitivity to interconn, interRRE
April 22, 2019

32. Responsiveness to latency and response size
Latency double
RSPDouble
April 22, 2019

33. Validation HTTP SQUID mbps pps mbps pps AUCK 3.33 591 0.55 58 SWING
3.24
551 57
Modeled parameters
Response size
median IQR median IQR
747
3371
1649
5224
735
3357
1423
5225
April 22, 2019

34. UDP Currently support two simple models
Bulk transfer
Constant bit rate transfer
For more complex, adaptive UDP protocols
Need to model application’s adaptability
April 22, 2019

35. Related work Harpoon, SURGE Tmix RAMP Tcplib
All applications in a single class, no network
Tmix
Replay of tcp connections
RAMP
Simulation based, only for HTTP and FTP
Tcplib
Ability to generate traffic but not to populate model
April 22, 2019

36. Importance of burstiness
At large timescales (mins-hours)
LRD
Self-similarity
Buffer size requirement of LRD traffic
At small timescales (ms-secs)
Loss rate at buffers for bursty traffic
Need to generate bursty traffic to understand its importance!
April 22, 2019

37. Changing application mix
Baseline comparison
from an earlier graph
April 22, 2019

38. Changing application mix
Increase SQUID traffic by 20 times
What should overall energy plot look like?
SQUID is different from overall AUCK
April 22, 2019

39. Changing application mix
Increase SQUID traffic by 20 times
What should overall energy plot look like?
Can project traffic demands to alternate scenarios
April 22, 2019

40. Validate Responsiveness?
Cannot, by definition
But …
Think baby steps, not giant leaps
Better than what you can do today
April 22, 2019

41. Fixed CDFs for trace duration?
Yes at the moment
Distributions stationary for a few minutes
Dynamically change in future
Changes to link bandwidth
Shift is application popularity
April 22, 2019

42. Generic Parameterization
Study lots of traces
Classify shape of CDF based on link-class
Curve-fitting/Analytical distributions
Use existing results from literature to mix and match
April 22, 2019