2. 2/16
Interactions of Overlay Routing
● Overlay, overlay, overlay!
○ Virtual network on top of IP network
● Vertical Interaction: Overlay vs. Underlay
○ Overlay: application-layer routing
○ Underlay: IP-layer routing
● Horizontal Interaction: Overlay vs. Overlay
○ Multiple overlays share the same underlay network
● Internal Interaction: Overlay node vs. Overlay node
○ Overlay nodes interact within a single overlay

3. 3/16
Contents
● Motivation
● Vertical Interaction Game
● Traffic Engineering (TE)
○ MPLS, Oblivious, COPE
● Overlay Routing
○ TE-Aware vs. Selfish
● Simulation
● Conclusion

4. 4/16
Motivation: An Analogy
● Consider a transportation network
● Traffic authority manages traffic lights
○ to make a good traffic flow
○ to avoid traffic jam
○ global view & manage the whole traffic
● Drivers choose their directions
○ to get to their destination ASAP
○ local view & care about its own drive
Authority vs. Drivers
TE vs. Overlay

5. 5/16
Vertical Interaction Game
TE : (Traffic Matrix) x (Topology) • (IP-layer Routing)
(IP Routing) x (Overlay Traffic) • (Overlay Latency)
Overlay: (Overlay Latency) x (Overlay Topology) • (Overlay Routing)
(Overlay Routing) x (Underlay Traffic) • (Traffic Matrix)
In game theory, a two-player non-cooperative repeated game

6. 6/16
Traffic Engineering (TE)
● Input: traffic matrix, (static) topology
● Output: physical routing (a.k.a. IP-layer, underlay)
● {fs,d (l) | s,d: node, l: link }
○ source-destination (s, d) and link l
○ fraction of demand (s, d) flowing through link l
● flow conservation law:
○ for each router,
○ total incoming traffic = total outgoing traffic

7. 7/16
Various TE Techniques
● MPLS Traffic Engineering
○ Adapt to the current traffic matrix (TM)
○ Minimize the Maximum Link Utilization (MLU)
● Oblivious Routing
○ Reasonable performance for all possible TMs
○ Optimal oblivious ratio
● COPE
○ Convex-hull-based Optimal TE with Penalty Envelope
○ A hybrid approach
○ Close-to-optimal performance for normal TM
○ Performance penalty bound with unpredicted TM

8. 8/16
Overlay Routing
● Input: link latency, (static) overlay traffic matrix
● Output: logical routing (a.k.a. overlay, application-
layer)
● {hps’t’ | s’, t’: overlay node, p: overlay path}
○ overlay node pair (s’, t’), path P
○ fraction of demand (s’, t’) flowing through path P
● logical flow conservation
sum of traffic in each path = total overlay demand

9. 9/16
Overlay Routing
● Selfish Overlay Routing
○ minimize total latency without any constraints
● TE-Aware Overlay Routing
○ (1) limit the selfishness so that overlay doesn’t bother TE
■ if current latency > threshold, run limited optimizer
■ minimize total latency
■ given specific link is not overloaded • lower TE’s MLU
○ (2) overlay even helps out TE in some case
■ if current latency < threshold, run load balancer
■ minimize overlay traffic demand • reduce traffic demands
■ given the current latency preserved

10. 10/16
Overlay 1: Selfish
● Selfish Overlay Routing
○ minimize total latency without any constraints

11. 11/16
Overlay 2: TE-Aware
● Load-Balancer Limited-Optimizer
Reduce
overlay traffic
Preserve current latency
Don’t overload a specific link
Reduce overlay
latency

12. 12/16
Simulation Settings
● 14-Node Tier-1 POP-to-POP Topology
- 4-node overlay network
● Synthetic Traffic Matrix with Gravity Model
- 10% of the whole traffic is controlled by
overlay

13. 13/16
MPLS vs. COPE under Selfish Overlay
● COPE is better than MPLS for both players
○ Overlay latency variance: MPLS (75) >> COPE (1)
○ TE MLU variance: MPLS (27%) >> COPE (5%)
● Adaptation may not be a good idea..

14. 14/16
Selfish vs. TE-Aware on top of MPLS
● Stable TE-aware overlay
○ Overlay latency variance: selfish (45) > TE-aware (15)
● Big MLU spikes with selfish overlay
○ TE MLU variance: selfish (25%) >> TE-aware (5%)

15. 15/16
Simulation Summary
85~86
(2)
80~95
(15)
85~86
(1)
67~107
(40)
overlay
latency
51~55%
(4%)
50~53%
(3%)
50~55%
(5%)
50~77%
(27%)
TE’s
MLU *
TE-aware
on COPE
TE-aware
on MPLS
Selfish on
COPE
Selfish on
MPLS
● *MLU: Maximum Link Utilization
● 10% of traffic by overlay

16. 16/16
Conclusion and Future Direction
● We improve “vertical interaction” between overlay routing and
traffic engineering
○ Adaptation may not be good for TE in the presence of dynamic
overlay traffic
○ Overlay has incentives to be TE-aware to get better performance
● We will enhance the model in several directions
○ Vertical interaction in inter-domain level
○ Vertical interaction + Horizontal interaction (multiple overlays)
○ Overlay routing oblivious to underlay routing

18. 18/16
Selfish vs. TE-Aware on top of COPE
● Both overlays work well on top of COPE
● (Selfish on MPLS) vs. (TE-aware on COPE)
○ Overlay latency variance: 68~108 vs. 85~87
○ TE MLU variance: 50%~77% vs. 51% ~55%

19. 19/16
Implementation
● GAMS + Perl Scripts
● GAMS: General Algebraic Modeling System
○ a language for linear, non-linear optimization problem
formulation
○ interface to various LP, NLP solvers
○ www.gams.com
● Condor for intensive computation
○ takes hours to run the solve the optimization problems
○ www.cs.wisc.edu/condor

20. 20/16
Related Works
● Lili Qiu, Yang Richard Yang, Yin Zhang, and Scott Shenker. “On Selfish
Routing in Internet-Like Environments”, SIGCOMM 2003.
○ first introduce the vertical interaction
○ show MPLS has better interaction than OSPF
● Yong Liu, Honggang Zhang, Weibo Gong and Don Towsley, “On the
Interaction Between Overlay Routing and Traffic Engineering”, INFOCOM
2005.
○ formulate the interaction as a non-cooperative game
○ prove the existence of Nash equilibrium
○ with simulation, show the poor interactions

21. 21/16
Vertical Interaction with OSPF
● From “Selfish Routing in the
Internet-like Environments”

22. 22/16
Vertical Interaction with MPLS
● From “Selfish Routing in the
Internet-like Environments”

23. 23/16
Vertical Interaction Game
TE determines the physical routing • link latency
Overlay changes the logical routing • traffic demand

24. 24/16
TE1: Adaptive MPLS

25. 25/16
TE2: Oblivious Routing

26. 26/16
TE3: Hybrid - COPE