Employing Agent-based Models
to study Interdomain Network
Formation, Dynamics &
Economics
Aemen Lodhi (Georgia Tech)
Workshop on Internet Topology & Economics (WITE’12)
1
Outline
• Agent-based modeling for AS-level
Internet
• Our model: GENESIS
• Application of GENESIS
– Large-scale adoption of Open peering
strategy
• Conclusion
2
What is the environment that we
are we trying to model?
• Autonomous System level Internet
• Economic network
Internet
Transit
Provider
Enterprise
customer
Transit
Provider
Content
Provider
Content
Provider
Enterprise
customer
3
What is the environment that we
are we trying to model?
• Complex, dynamic environment
– Mergers, acquisitions, new entrants, bankruptcies
– Changing prices, traffic matrix, geographic
expansion
•
•
•
•
Co-evolutionary network
Self-organization
Information “fuzziness”
Social aspects: 99% of all peering relationships
are “handshake” agreements*
*”Survey of Characteristics of Internet
Carrier Interconnection Agreements 2011” – Packet Clearing House
4
What are we asking?
• Aggregate behavior
– Is the network stable?
– Is their gravitation towards a particular
behavior e.g., Open peering
– Is their competition in the market?
• Not so academic questions
– Is this the right peering strategy for me?
– What if I depeer AS X?
– Should I establish presence at IXP Y?
– CDN: Where should I place my caches?
5
Different approaches
• Analytical / Game-theoretic approach
• Empirical studies
• Generative models e.g., Preferential
attachment
• Distributed optimization
• Agent-based modeling
6
Why to use agent-based modeling?
• Incorporation of real-world constraints
– Non-uniform traffic matrix
– Complex geographic co-location patterns
– Multiple dynamic prices per AS
– Different peering strategies at different
locations
• Scale – hundreds of agents
• What-if scenarios
• Understanding the “process” and not just the
“end-state”
7
Why not to use agent-based
modeling?
• Large parameterization space
– Systematic investigation of full parameter
space is difficult
• Validation
• Computational cost
• Under some circumstances reasoning
may be difficult e.g. instability in a
model with hundreds of agents
8
GENESIS
9
The model: GENESIS*
• Agent based interdomain network formation
model
• Fundamental unit: An agent (AS) with
economic interests
• Incorporates
– Co-location constraints in provider/peer
selection
– Traffic matrix
– Public & Private peering
– Set of peering strategies
– Peering costs, Transit costs, Transit revenue
*Aemen Lodhi, Amogh Dhamdhere, Constantine Dovrolis, “GENESIS: An agent-based
model of interdomain network formation, traffic flow and economics,” InfoCom 2012
10
Peering link at top
tier possible
Geographic
Link formation
across regions
across overlap
geography not
possible
Geographic presence & constraints
Regions
corresponding
to unique IXPs
11
The model: GENESIS*
Fitness = Transit Revenue – Transit Cost – Peering cost
• Objective: Maximize economic fitness
• Optimize connectivity through peer and
transit provider selection
• Choose the peering strategy that
maximizes fitness
Peering strategies
• Restrictive: Peer only to avoid network
partitioning
• Selective: Peer with ASes of similar size
𝑉𝑥
≤𝜎
𝑉𝑦
𝑉𝑥 = 𝑇𝑟𝑎𝑛𝑠𝑖𝑡 + 𝐺𝑒𝑛𝑒𝑟𝑎𝑡𝑒𝑑 + 𝐶𝑜𝑛𝑠𝑢𝑚𝑒𝑑
• Open: Every co-located AS except customers
• Choose peering strategy that is predicted to give
maximum fitness
13
Peering strategy adoption
Open
Selective
1
2
Open
3
Time
Depeering
Peering Transit
Provider selection
• No coordination, limited foresight
• Eventual fitness can be different
• Stubs always use Open peering strategy
14
Application of GENESIS:
Analysis of peering strategy
adoption by transit providers in
the Internet*
*Aemen Lodhi, Amogh Dhamdhere, Constantine Dovrolis, “Analysis of peering strategy
adoption by transit providers in the Internet,” NetEcon 2012
15
Motivation: Existing peering
environment
• Increasing fraction of interdomain traffic
flows over peering links*
• How are transit providers responding?
Transit
Provider
Content
Provider/CDN
Access
ISP/Eyeballs
*C. Labovitz, S. Iekel Johnson, D. McPherson, J. Oberheide and F. Jahanian,
“Internet Interdomain Traffic,” in ACM SIGCOMM, 2010
16
Motivation: Existing peering
environment
• Peering strategies of ASes in the Internet
(source: PeeringDB www.peeringdb.com)
• Transit Providers peering openly ?
17
Approach
• Agent based computational modeling
• Corroboration by PeeringDB data
• Scenarios
Without-open
• Selective
• Restrictive
vs.
*Stubs always use Open
With-open
• Selective
• Restrictive
• Open
Percentage of transit providers
Strategy adoption by transit
providers
100
90
80
70
60
50
Restrictive
40
Selective
30
Open
20
10
0
Without-open
Conservative
With -open
Non-conservative
Scenarios
19
Collective impact of Open peering on
fitness of transit providers
• Cumulative fitness reduced in all simulations
20
Impact on fitness of individual transit
providers switching from Selective to Open
• 70% providers have their fitness reduced
21
Why do transit providers adopt
Open peering?
Affects:
• Transit Cost
• Save
Transit
Revenue
transit
• Peering
costs Cost
v
x
y
But your
customers are
doing the same!
z
w
Why gravitate towards Open
peering?
x regains lost
x adopts Open
Options
for
transit
revenue
peering
x?
partially
x lost
transit
revenue
z
w,
traffic
passes
through x
again!
x
y
Not isolated Y peering openly
decisions
Network
effects
!!
z
w,
z
z
y,
traffic
bypasses
x
w
Conclusion
• Employ agent-based models for largescale study of interdomain network
formation
• Parameterization and validation are
difficult
• Agent-based models can reveal
surprising behavior
24
Conclusion
• Gravitation towards Open peering is a
network effect for transit providers
(70% adopt Open peering)
– Economically motivated strategy selection
– Myopic decisions
– Lack of coordination
• Extensive Open peering by transit
providers in the network results in
collective loss
25
Thank you
26