1. No category

The Network Neutrality
Debate:
An Engineering Perspective
Vishal Misra
Columbia University, in the
City of New York
Joint work with Richard
(Tianbai) Ma, Dahming Chiu,
John Lui and Dan Rubenstein
Conversation between a prominent Economist and Dave
Clark (Foundational Architect of the Internet)
❖
Economist: “The Internet is about routing money.
Routing packets is a side-effect.”
❖
Economist: “You really screwed up the money-routing
protocols”.
❖
Dave: “We did not design any money-routing protocols”.
❖
Economist: “That’s what I said”.
Rest of the talk
❖
Background
❖
Cooperative Games and Shapley Values
❖
Application of Shapley Values to Peering
❖
Instability of settlement free peering
❖
Analysis of Paid Prioritization
❖
Monopoly
❖
Oligopoly
❖
Public Option
ISP Settlements and Shapley Values
The P2P Battlefield: Engineering and Economics
❖
❖
Proposed engineering approaches:
❖
ISPs: Drop P2P packets based on port number
❖
Users: Dynamic port selection
❖
ISPs: Deep packet inspection
❖
Users: Disguise by encryption
❖
ISPs: Behavioral analysis
Comcast started throttling BitTorrent traffic
It became evident to us the problem was rooted in
Economics, not Engineering
What were the Economists saying?
Building blocks of the Internet: ISPs
•
The Internet is operated by thousands of interconnected
Internet Service Providers (ISPs).
•
An ISP is an autonomous business entity.
– Provide Internet services.
– Common objective: to make profit.
ISP
ISP
ISP
Three types of ISPs
1.
Eyeball (local) ISPs:
–
Provide Internet access to residential users.
–
E.g. Time Warner Cable, Comcast, Verizon, AT&T
2.
Content ISPs:
–
Serves content providers
–
E.g. Cogent, Akamai, Level3, Netflix (Content Distribution
Networks)
3.
Transit ISPs:
–
Provide global connectivity, transit services for other ISPs.
–
E.g. Tier 1 ISPs: Level3, AT&T, Telefonica, Tata
C
ISP
T
ISP
B
ISP
Cooperative Games
Players: N
Coalition: A
Value: V(A)
Coalition: B
Value: V(B)
Coalitions
Value: V
Cooperative Game Theory
• Analyses coalition formation given value allocation
• Value allocation characterizes a solution of a game
• Some properties of interest in a solution
• Stability: Players do not want to deviate from the solution • Fairness: Allocation to players reflects their contribution
Convex Games
• V is Convex if for all coalitions A, B,
V(AUB)-V(B) ≥ V(A)-V(A∩B)
• Marginal contribution of a player
increases with the size of the
coalition it joins
• Natural model for networks
• Metcalfe’s “law” V(n) = n2
• Odlyzko’s “law” V(n) = n log n
Core and Shapley Value of Convex Games
Unstable Solutions
Shapley Value
Stable Solutions
(Core)
Stability of the Shapley value
V({1}) = a, V({2}) = b
V({1,2}) = c > a + b.
•
Convex game:
– V(SUT)>= V(S)+V(T)
– Whole is bigger than the sum of
parts.
Stability of the Shapley value
V({1}) = a, V({2}) = b
V({1,2}) = c > a + b.
•
Convex game:
– V(SUT)>= V(S)+V(T)
– Whole is bigger than the sum of
parts.
•
Core: the set of efficient
profit-share that no coalition
can improve upon or block.
Stability of the Shapley value
V({1}) = a, V({2}) = b
V({1,2}) = c > a + b.
•
Convex game:
– V(SUT)>= V(S)+V(T)
– Whole is bigger than the sum of
parts.
•
Core: the set of efficient
profit-share that no coalition
can improve upon or block.
•
Shapley [1971]
– Core is a convex set.
– The value is located at the center
of gravity of the core.
Axiomatic characterization of the Shapley value
What is the Shapley value? – A measure of one’s contribution to
different coalitions that it participates in.
Shapley Value
Shapley 1953
Efficiency
Symmetry
Dummy
Additivity
Myerson 1977
Efficiency
Symmetry
Fairness
Efficiency
Symmetry
Strong Monotonicity
Young 1985
Efficiency, Symmetry
Symmetry:All
Identical
Efficiency:
Profit
players
getPlayers
equal
goes
to the
shares
Balanced Contribution (Fairness)
How do we share profit? -- the baseline case
C1
B1
•
One content and one eyeball ISP
•
Profit V = total revenue = content-side + eyeball-side
•
Fair profit sharing:
1
ϕB= ϕC=
V
1 2
1
How do we share profit? -- two symmetric eyeball ISPs
B1
C1
B2
Axiomatic Solution:
• Symmetry: same profit for symmetric eyeball ISPs
ϕB= ϕB2= ϕB
1
• Efficiency: summation of individual ISP profits equals V
ϕC+1 2ϕB= V
• Fairness: same mutual contribution for any pair of ISPs
1
ϕC− V = ϕB−
0
1
1
2
Unique solution
(Shapley value)
2
ϕC= V
1 3
1
ϕB = V
6
How do we share profit? -- n symmetric eyeball ISPs
B1
C1
B2
Bn
•
Theorem: the Shapley profit sharing solution is
1
n
ϕB=
V, ϕC=
V
n(n+1)
n+1
Results and implications of profit sharing
1
n
ϕB=
V, ϕC=
V
n(n+1)
n+1
•
With more eyeball ISPs, the content ISP
gets a larger profit share.
B1
C1
– Multiple eyeball ISPs provide redundancy
– The single content ISP has leverage.
•
•
Content’s profit with one less eyeball:
The marginal profit loss of the content ISP:
Bn-1
n-1
ϕC=
V
n
ΔϕC= n-1 V - n V = - 12 ϕC
n
n+1
n
If an eyeball ISP leaves
– The content ISP will lose 1/n2 of its profit.
– If n=1, the content ISP will lose all its profit.
Bn
Profit share -- multiple eyeball and content ISPs
•
C1
B1
C2
B2
Cm
Bn
Theorem: the Shapley profit sharing solution is
m
n
ϕB =
V, ϕC=
V
n(n+m)
m(n+m)
Results and implications of ISP profit sharing
m V
n V
ϕB= n (n+m) , ϕC = m(n+m)
•
Each ISP’s profit share is
– Inversely proportional to the number
of ISPs of the same type.
– Proportional to the number of ISPs
of the other type.
•
C1
B1
C2
B2
Cm
Bn
Intuition
– When more ISPs provide the same service, each of them
obtains less bargaining power.
– When fewer ISPs provide the same service, each of them
becomes more important.
Profit share -- eyeball, transit and content ISPs
•
C1
T1
B1
C2
T2
B2
Cm
Tk
Bn
Theorem: the Shapley profit sharing solution is
Common ISP Business Practices: A Macroscopic View
Two forms of bilateral settlements:
Zero-Dollar
Peering
T
ISP
$$$
T
ISP
Provider ISPs
$$$
Customer-Provider
Settlement
B
C
ISP
Customer ISPs
ISP
Achieving A Stable Solution: Theory v Practice
Shapley
Reality
T
T
ISP
ISP
B
C
ISP
ISP
Implications
•
When CR ≈ BR, bilateral implementations:
– Customer-Provider settlements (Transit ISPs as providers)
– Zero-dollar Peering settlements (between Transit ISPs)
– Common settlements can achieve fair profit-share for ISPs.
•
If CR >> BR, bilateral implementations:
– Reverse Customer-Provider (Transits compensate Eyeballs)
– Paid Peering (Content-side compensates eyeball-side)
– New settlements are needed to achieve fair profit-share.
•
When Customer Side Competition << Content Side Competition
– Paid Peering Will Dominate
31
Paid Prioritization
Network Neutrality (NN)
Better!
Happy?
Paid Prioritization (PP)
Happier?
Some earlier analysis of paid prioritization
Treat the “Internet” as an M/M/1 Queue
Queue Size = QoS
User-side: 3 Demand Factors
❒ Demand Function
Fractional Loss of Users vs Fractional Loss of Throughput
System Side: Rate Allocation
Uniqueness of Rate Equilibrium
❒ ISP Paid Prioritization
Capacity
Premium Class
Charge
Ordinary Class
Monopolistic Analysis
❒ Utilities (Surplus)
❒ Type of Content
Monopolistic Analysis
❒ Regulatory Implications
❒ Oligopolistic Analysis
❒ Duopolistic Analysis
Duopolistic Analysis: Results
❒ Oligopolistic Analysis: Results
❒ Regulatory Preference
Monopoly
Oligopoly
ISP market structure
User Utility
Wireless Net Neutrality
Differences in the wireless scenario
❖
Payment model is different
❖
❖
Bandwidth a lot more scarce
❖
❖
Even if we auction of all remaining spectrum (40%), it only covers one
year of bandwidth growth (doubling every year)
“Zero rated” (bandwidth usage doesn’t count against caps) is a big problem
❖
❖
Largely metered. Wired consumers largely unlimited
“Good guys” in the wired world (Google, Facebook etc.) are promoting
“zero rated” apps in the wireless world
“Last-mile” is less of a problem. Creating competition easier
❖
Multi-homing via AppleSim a first step
References
❖
Richard T. B. Ma, Dah Ming Chiu, John C. S. Lui, Vishal Misra and Dan Rubenstein. On
Cooperative Settlement Between Content, Transit and Eyeball Internet Service Providers. IEEE/
ACM Transactions on Networking, Volume 19, Issue 3, pp. 802 - 815, June, 2011. Extended
Version of CoNEXT 2009 paper. Featured in IEEE ComSoc Technology News, June 2014, Special
Issue on Network Neutrality, the Internet & QoS.
❖
Richard T. B. Ma, Dah Ming Chiu, John C. S. Lui, Vishal Misra and Dan Rubenstein. Internet
Economics: The Use of Shapley Value for ISP Settlement. IEEE/ACM Transactions on Networking,
Volume 18, Issue 3, pp. 775 - 787, June, 2010.
❖
Richard T. B. Ma and Vishal Misra. Congestion and Its Role in Network Equilibrium. IEEE Journal
on Selected Areas in Communications, Volume 30, Issue 11, pp. 2180 - 2189, December, 2012.
❖
Richard T. B. Ma and Vishal Misra. The Public Option: A Non-Regulatory Alternative to Network
Neutrality. IEEE/ACM Transactions on Networking, Volume 21, Issue 6, pp. 1866 - 1879,
December, 2013. Featured in IEEE ComSoc Technology News, June 2014, Special Issue on
Network Neutrality, the Internet & QoS.
❖
Richard T. B. Ma, John C. S. Lui and Vishal Misra. Evolution of the Internet Economic
Ecosystem. IEEE/ACM Transactions on Networking, to appear.