1. Economic incentives for fair and efficient ISP settlements
Tianbai Ma
Department of Electrical Engineering
Columbia University
Thanks. It’s a great honor to present my work here. This is joint work with DM Chiu and John Lui from the Chinese University of HK and Vishal Misra and Dan Rubenstein from Columbia University.

2. General statement of research Macro perspective ISP settlement problem
Outline
General statement of research
Macro perspective ISP settlement problem
Micro perspective ISP settlement problem
My other research – past and future
This talk has three parts. In the first two parts, I’ll discuss the ISP settlement problem from a Macro and a Micro perspective. In the last part, I’ll describe the research of network economics in general.

3. Internet: the design and the reality
Was designed as an engineering system to achieve global objectives:
Efficient communication system
Quality of services for users
Fairness among users
Has evolved to be a social-economic system with individual objectives:
Bring business (Corporations)
Reduce costs (Internet operators)
Make Profit (Almost everyone)

4. General Statement of Research
Global Objectives (Engineering Goals)
Individual Objectives (Economic Incentives)
Economic
Mechanism
Efficiency
Fairness …
High Profit
Low Cost …
Problem:
Individual objectives do not align with global objectives.
Economic incentives fail the engineering design goals of Internet.
Objective: Reconcile individual incentives with global objectives.
Approach: Design an economic mechanism
to address individual incentives,
to achieve global objectives.

5. A View of Internet Service Providers (ISPs)
The Internet is operated by hundreds of Internet Service Providers (ISPs).
An ISP is a business entity.
Comprise multiple sub-networks.
Provide Internet services.
Objective: maximize profit.
ISP
We all know that the Internet is composed of thousands of Autonomous Systems (ASes). An ISP is a business entity that might comprises multiple ASes. They provide Internet services for individual users as well as corporations. They all have the same objective, which is to maximize their own profit.

6. Different Classes of ISPs
Eyeball ISPs:
Provide Internet access to individual users.
Place Large investment in infrastructure.
E.g. AT&T, Verizon … HK ISPs
Content ISPs:
Provide contents via the Internet.
Serve customers like:
Transit ISPs:
Tier 1 ISPs: global connectivity of the Internet.
Provide transit services for other ISPs.
Cover a large geographic area.
According to the type of customers that an ISP serves, we can classify ISPs into three categories. Eyeball ISPs provide Internet access to individual users. They usually invest heavily on network infrastructures. Examples are AT&T and Verizon. Content ISPs, on the other hand, serve customers which are content providers, such as Google/Yahoo and content distribution networks like Akamai. Transit ISPs are typically Tier 1 ISPs that provide the global connectivity of the Internet. Normally, they cover a large geographic range.

7. Current ISP Business Practices: A Macro Perspective
Two forms of bilateral settlements:
Customer-Provider Settlement
Zero-Dollar Peering Settlement
Transit ISP
Transit ISP
From a Marco-perspective, ISPs often maintain the following two bilateral settlements. Under the zero-dollar peering, Transit ISPs forward each other’s traffic without payment. The customer/provider settlement often involves a transit ISP playing the role of a provider to help other ISPs forward traffic and get paid from them.
Eyeball ISP
Content ISP

8. ISP Positions on Current Settlement
Transit
Eyeball
However, not all ISPs are satisfied with the current settlements. For example, due to the zero-dollar peering, Transit ISPs always feel that other ISPs are free-riding on their facilities. For the eyeballs ISPs, the major revenue source is the monthly payments from end-customers. They feel that they should be compensated more since they’ve invested heavily to bring the customers from whom other ISPs can generate revenue. However, content providers refuse to pay eyeball ISPs. They claim they’ve paid their faire share for transit and delivery to content ISPs.
Content Providers

9. Issues of Current ISP Settlement: A Macro Perspective
Net Neutrality Debate: Provide Content-based Service Differentiation ?
Yes No
Eyeball
Transit
Content Providers
Network Balkanization: De-peering between ISPs
Different ISP opinions bring a couple of issues. The first is the network neutrality debate, which argues whether content-based service differentiations should be provided for the Internet. Transit and eyeball ISPs support to provide differentiated services in order to generate more revenue. Content providers, on the other hand, advocate the neutrality of the network which prohibits the discriminations on delivering different contents through the network. Another issue relates to the zero-dollar peering. In 2005, Level 3 disconnected all the peering links with Cogent, resulting at least 15% of the Internet unreachable for the customers of both ISPs at that time. In order to solve these problems, we first need to answer the question “how to appropriately share profit amongst ISPs?” Without answering this question, ISPs don’t have enough incentives to cooperate by interconnecting or to provide better services for customers. In the first part of this talk, we try to answer this question.
Transit
Transit
zero-dollar peering
15% of Internet Unreachable
How to appropriately share profit amongst ISPs?

10. Network Model: Eyeball Side Definitions
Geographic Regions (r)
Per Customer Internet Charge (ar)
Customer Size (Xr)
Eyeball ISP (Bj)
Revenue from a region r (arXr)
r= ¥
Here, we describe our network model. We start from the eyeball ISPs. We consider that the end-customers come from different geographic regions, denoted as r, e.g. the US and the UK. The per customer Internet access fees in different regions are denoted as alpha. We denote X as the customer size of each region. We consider a set of eyeball ISPs serve the customers of each region. Since each customer pays alpha, the revenue generated from a region is alpha times the customer size X. The total eyeball-side revenue is the summation of revenues from different regions.

11. Customer Behavior: Demand Assumptions
Two types:
Elastic intra-region demand
Customers can switch among ISPs within a region.
New ISPs may take customers from other ISPs in the same region.
Customers move to other ISPs when the original eyeball ISP leaves the system.
Inelastic inter-region demand
Customers cannot switch to ISPs in other regions.
Constant customer size in a region.
Next, we make some assumptions about the customer demand. We assume that the intra-region customer demand is elastic. This means that customers can switch from one ISP to another within a region. A new ISP may attract customers previously served by other ISPs in the region. When an ISP leaves the region, its customers will shift to other ISPs in the same region. We also assume that the inter-region customer demand is inelastic. This means that customers are not able to switch between ISPs in different regions due to geographical limitations and again the total customer size in a region is a constant.

12. Network Model: Content Side Definitions
Content Items (q)
Content ISP (Ci)
Per Customer Revenue for uploading content q (bq)
Revenue generated by uploading content q to region r (bqXr)
Then we move on to the content side. We assume all customers are interested in obtaining a set of content items, e.g. a music file and a poker game, from the Internet. We consider a set of content ISPs, each of which provides a subset of the content items. For each item q, we define beta_q as the per customer revenue the content provider pays the content ISPs for uploading the content. Therefore, the revenue generated by uploading file q to region r is beta_q times X_r. The total content-side revenue equals beta times the total customer size from all regions. Again, the question is “how to share profits among ISPs?”
How to share profits amongst ISPs?

13. How to share profit? -- the baseline case
One content and one eyeball ISP.
One region, US, and one content, ♫ .
Profit v = = Revenue – Cost
Egalitarian profit sharing:
We start from the simplest case with one eyeball and one content ISP. We also assume there are only one region and one content item. We define the profit v as the total revenue. Here, we use profit and revenue interchangeably, assuming the costs are negligible. We will include costs when we look into a Micro perspective in a later part of this talk. Because without the other party’s help, each ISP obtains zero profit, the fairest solution is for both ISPs to evenly share the profit.

14. How to share profit? -- two eyeball ISPs
Desirable properties:
Symmetry: same profit for symmetric eyeball ISPs
Efficiency: summation of individual ISP profits equals v
Fairness: same mutual contribution for any pair of ISPs
Next we consider the case where we have two symmetric eyeballs serving the region. First, we want our solution to be unbiased in the sense that the two symmetric eyeballs obtain the same amount of profit. We call this the symmetry property. Second, we require the summation of the individual profits to be exactly v. We call this the efficiency property. Third, we require the mutual contribution of one ISP to another to be the same for any pair of ISPs. For example, we pick up an eyeball and a content ISP. Suppose one eyeball leaves the system, all customers will shift to the other eyeball. The total profit of the system does not change and the content’s appropriate profit will be one half of v; however, if the only content leaves the system, the eyeballs obtain zero profit. We require the marginal profit between any pair of ISPs to be equal. We call this the fairness property. Now we have two unknowns and two equations, which uniquely determine the profit for each ISP. And this solution is also known as the Shapley value solution originated from Coalition Game Theory.
Unique solution
(Shapley value)

15. History and Properties of the Shapley Value
Efficiency
Symmetry
Dummy
Additivity
Shapley 1953
Efficiency
Symmetry
Fairness
Myerson 1977
Efficiency
Symmetry
Strong Monotonicity
Young 1985
Here we briefly introduce the history and properties of the Shapley value. Lloyd Shapley first characterized the solution by Efficiency, Symmetry, Dummy and Additivity properties. Later, Myerson used Fairness property to characterize the value. Young showed that the value can also be characterized by a Strong Monotonicity property. We will mention some of the properties throughout this talk. Notice, the Shapley value satisfies all six properties and the subsets of these properties uniquely determine the Shapley value solution.
Van den Nouweland et al. 1996
Application in Telecommunication
Ma et al. 2008
Ma et al. 2007
ISP Routing Incentive
ISP Profit Sharing
ISP Interconnecting Incentive

16. How to share profit? -- multiple eyeball ISPs
n eyeball ISPs.
Let’s continue our discussion. We can extend the result to the case where we have n eyeball ISPs serving the region. To satisfy the desirable properties, we uniquely determine the profit sharing solution for ISPs as the following.
The unique solution (Shapley value) that satisfies Efficiency Symmetry and Fairness:

17. Results and implications of profit sharing
More eyeball ISPs, the content ISP gets larger profit share.
Elastic users move between eyeball ISPs.
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:
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.
Let’s get a closer look at this solution. It suggests that the more of eyeball ISPs, the larger profit for the content ISP. Intuitively, this is because when an eyeball leaves, its customer can always go to another eyeball, and there is no revenue loss. So multiple eyeballs provide redundancy and the only content ISP can leverage. Using the formula, we can derive the fair share of the content ISP with one less eyeball ISP in the system. Then we can measure the marginal profit loss of the content ISP contributed by an eyeball ISP. The result says when an eyeball leaves the system, it will only cost one over n squared of the content’s original profit. In particular, if n=1, when the only eyeball leaves the system, it will cost the content ISP all its original profit. I am not sure if the following analogy is appropriate, but just imagine the only content ISP as Windows and the multiple eyeball ISPs as compatible processor-producer. The result explains why Microsoft naturally dominates the market and obtains high profit margin.

18. Profit share -- multiple eyeball and content ISPs
Next, we derive the profit sharing solution for the case where we have multiple and eyeball and content ISPs. Again, the customer demands are elastic and the same content can be obtained from any of the content ISPs.
The unique solution (Shapley value) that satisfies Efficiency Symmetry and Fairness:

19. Results and implications of ISP profit sharing
Each ISP’s profit share is
Inversely proportional to the number of ISPs of its own type.
Proportional to the number of ISPs of the opposite type.
This solution says that each ISP’s profit is inversely proportional to the number of ISPs of its kind, and proportional to the number of ISPs of a different kind. Same as before, the more of a same kind of ISP provides redundancy, therefore, each of them has less bargaining power; and the fewer of a same kind of ISP can leverage. This solution generalizes the previous results where m=1.
Intuition
The larger group of ISPs provides redundancy.
The smaller group of ISPs has leverage.

20. Profit share -- eyeball, transit and content ISPs
Intuition
The larger group of ISPs provides redundancy.
The smaller group of ISPs has leverage.
A further generalization is to consider a group of Transit ISPs located in between of the eyeball and content ISPs. The Shapley value solution becomes more complicated.

21. Profit share -- multiple regions and multiple items
Up till now, we have limited ourselves to deal with one region with a single content item provided by all content ISPs. Now we consider how to make profit share when we have multiple items for customers in multiple regions. As we mentioned earlier that the Shapley value satisfies an additivity property. By this property, we can first identify the revenue components as follows, and then distribute each of them separately.
Revenue sources are separable (Additivity property)
Eyeball-side components:
Content-side components:

22. Profit share -- multiple regions and multiple items
In this figure, we illustrate the profit share for this revenue component as an example. This revenue is generated by providing the poker game to the US customers. Naturally, content ISPs that provide the poker game share the profit. Eyeball ISPs that serve the US region share the profit. And Transit ISPs that help the delivery share the profit. Eyeballs in a different region do not share the profit, because the inter-region demand is inelastic and they do not affect the this profit. Similarly, a different content can be considered as an inelastic content because customers cannot obtain the poker game from a content ISP that only providers the music file.
A specific revenue component is shared by
Content ISPs that provide the item
Eyeball ISPs that generates the revenue
Transit ISPs that help the delivery

23. Profit share – general topologies
In this slide, we illustrate a general procedure to calculate the Shapley value for an ISP under general topologies. We use the following example and try to calculate the Shapley value of C1. First, we consider any sub-topology where one of the ISPs is missing and we derive the Shapley value of C1 under these topologies. Second, we check whether the profit can still be generated without C1’s presence. If not, we say C1 is veto. Based on these information, we can apply the following formula. N denotes the # of ISPs; the last term is an indicator function of whether C1 is veto. Notice, since the function depends on the Shapley values of sub-topologies, we can implement a Dynamic Programming procedure to solve the Shapley value for general topologies.
Dynamic Programming!

24. Implications – the value chain$ $ $
Eyeball-side Revenue $
Content-side Revenue $ $ $ $ $ $ $ $ $ $
After getting the theoretic profit share, let’s explore its implications for ISP settlements. We focus on the three groups of ISPs. We know that Eyeball and Content ISPs receive end payments from customers and content providers. They keep their fair share of the revenue and forward the remaining. The Transit ISPs receive revenues from both sides and keep their own portion and keep forwarding the remaining revenue to the opposite side.

25. Implications – the value chain
Eyeball-side Revenue $
Content-side Revenue $ $ $ $ $ $ $
We can clearly see these two revenue flows.
Revenue Flows
Content-side revenue (CR): Content Transit Eyeball
Eyeball-side revenue (ER): Eyeball Transit Content

26. Implications – equivalent bilateral settlements
Customer
Provider
Customer
Eyeball-side Revenue $
Content-side Revenue $ $ $ $
Zero-dollar
Peering $ $ $
Now, we consider how we can achieve the same solution using bilateral settlements. We first consider the case where the amount of content-side revenue is close to that of the eyeball-side as illustrated here. We can simply make the net money exchange between the Transit ISPs and the other two groups of ISPs. The resulting bilateral settlements are in the form of customer/provider relationship, where the Transit ISPs are the providers, and the zero-dollar peering between transit ISPs, where they keep their own revenues. This explains that when local ISPs were not so different from each other, these two kinds of settlements were stable, because they achieved a solution which is close to the Shapley value solution.
When CR ≈ BR, bilateral implementations:
Customer-Provider settlements (Transit ISPs as providers)
Zero-dollar Peering settlements (between Transit ISPs)
Stable structure when local ISPs are homogeneous.

27. Implications – equivalent bilateral settlements$ $ $ $ $ $ $ $ $
Eyeball-side Revenue $ $ $ $ $ $
Customer
Provider $ $
Paid Peering
Content-side Revenue $ $ $
However, with the fast business development of the Internet, the content-side revenue is considerably much more than the fixed payments from eyeball side as illustrated here. After making the net money exchange again, the resulting bilateral settlements show that Transit ISPs need to compensate the Eyeball ISPs, which creates an inverse customer/provider relationship. In addition, since the payments from both sides are imbalanced, content-side ISPs need to compensate eyeball-side ISPs, which creates a paid-peering relationship. Although these new bilateral settlements largely deviate from the current settlements, we envision them to emerge so as to maintain stable settlements among ISPs.
If CR >> BR, bilateral implementations:
Reverse Customer-Provider: Transits compensate Eyeballs.
Paid Peering: Content-side compensates eyeball-side.
New settlements will emerge to maintain a stable structure.

28. Macro perspective ISP settlement: result summary
Content-Transit-Eyeball ISP model
Closed-form Shapley value for regular topologies.
Dynamic Programming for general topologies.
Implications for current bilateral settlements
Transit ISPs might need to compensate Eyeball ISPs, which creates a Reverse Customer-Provider settlement.
Paid Peering settlement may exist among Transit ISPs.
Guideline for
ISPs: negotiate stable and incentive settlements.
Government: make regulatory policy for the industry.
Here we make a summary before moving on to the second part. By using a CTE model, we derive closed-form Shapley value solutions and a DP procedure for general topologies. Based on these results, we draw some implications on the current bilateral settlements. We believe that the Shapley value solution serves a good guideline for ISPs to negotiate stable settlements and for government to make regulatory policy for the industry.

29. What have we done? – A Macro perspective
A Short Recap
What have we done? – A Macro perspective
A central authority’s view to distribute profit among ISPs and make appropriate settlements.
What are we going to do? – A Micro perspective
Analysis of ISP selfish behaviors and strategies and their effects on the entire network.
Questions to answer
How will ISPs behave if the profits are distributed according to the Shapley value solution?
How does that affect the entire network?
Up till now, we’ve looked at the ISP problem from a macro-perspective, which is a central authority’s view to make appropriate profit share among ISPs. As we mentioned, ISPs are selfish business entities, which might have conflicting objectives with other ISPs as well as the central authority. So in the second part, we’re going to analyze the selfish behaviors and strategies of ISPs. In particular, two question we will answer are “how ISPs will behave if profit is distributed by the Shapley value”, and “how does that affect the whole network”?

30. Current ISP Business Practices: A Micro Perspective
Provider ISP
Three levels of ISP decisions
Interconnecting decision E
Routing decisions R (via BGP)
Bilateral settlements f
Interconnection withdrawal
Settlement f affects E, R
Customer-Provider
Settlements
provider charges high
Hot-potato Routing
Route change
Source
From now on, we consider more generic ISPs, which might have customers of individual users, corporations or a combination of them. From a Micro-perspective, ISPs make three levels of decisions to maximize their profits. First, ISPs make interconnecting decisions. They decide whether or not to connect with a neighboring ISP. We denote the interconnecting decisions as E, which can be considered as the set of links of the topology, because these decisions form the network topology. After the ISPs are connected, they make routing decisions. They exchange BGP advertising routes and based on their own preferences to choose routing paths. For example, an ISP has some traffic to send to a destination. It can choose a shortest path going through the two routers of its own, or it can use hot-potato routing, choosing the nearest egress point, and make other ISPs forward the traffic. Because ISPs are selfish, the hot-potato routing is more common in practice. On top of routing and interconnecting, ISPs make bilateral settlements as mentioned before. Because the ultimate objective of each ISP is to maximize its profit, the settlements affect the underlying routing and interconnecting decisions. For example, due to the service charge, a customer-ISP might withdraw the connection with a provider ISP, and as a result, it has to route through its peering link.
Destination
Zero-Dollar
Peering
Shortest Path Routing
Customer ISP
Customer ISP

31. An ideal case of the ISP decisions
Route
Topology
An ideal case of the ISP decisions
Well-connected topology
Fixed Revenue
Backbone ISP 1
W = 1
Local ISP 1
Local ISP 2
A simple example:
Locally connect to
both backbone ISPs
Peering links at
both coasts
Two backbone ISPs
Two local ISPs
End-to-end service generates revenue
Routing costs on links
Here, we illustrate an ideal case of the ISPs’ decisions. Consider we have two backbone ISPs covering the whole United States. They have routers on both the east and the west coast. Both ISPs peer with each other at both sites. Suppose we also have two local ISPs, one on each side. They interconnect with both backbone ISPs. Now, we have a well-connected network. Assume the customers of these local ISPs want to communicate, and the service can generate a certain amount of revenue. We normalize the revenue to be one, which is represented by the green bar. Although we ignored the costs in the Macro-perspective models, we consider the routing cost here.
Backbone ISP 2

32. Routing cost model Assumptions (based on current practices)
x2/4
Assumptions (based on current practices)
Routing costs on links, e.g. bandwidth capacity and maintenance.
Going across the country is more expensive.
More expensive when link is more congested.
Costs increase with link loads
Standard queueing theory results.
Capital investment for upgrades.
The routing cost is a function of the traffic load going through the links. The routing cost relates to the bandwidth capacity and maintenance costs. We assume that the cost going across the country is more expensive then going locally, and the link cost is higher when the link gets more congested. These assumptions can be justified by standard queueing theory as well as the fact that after reaching a bottleneck capacity, capital investment is needed for upgrades. Here, we use a square function as an illustration; however, our model applies for general cost functions.

33. An ideal case of the ISP decisions
Route
Topology
An ideal case of the ISP decisions
Global Min Cost
Well-connected topology
MIN routing cost and MAX profit
Fixed Revenue
Cost | Profit
Backbone ISP 1
x22/4
W = 1
x12/16
x32/16
Local ISP 1
Local ISP 2
1/2
A simple example:
Two backbone ISPs
Two local ISPs
End-to-end service generates revenue
Routing costs on links
x42/8
x52/8
1/2
We normalize total required traffic load for the service to be one and we assume the following cost functions in this example. Now what we are concerned about is the total profit that can be generated. Ideally the global min-cost route minimizes the aggregate routing cost. This route evenly splits the traffic on these two paths. The red bars represent the routing costs on each link. For example, this cost is one-sixteenth, which is 1/2 squared divided by four. The aggregate routing cost is shown by the red bar and the green bar represents the profit all these ISPs can obtain. Notice that the total length of the bar is the constant revenue from the service. So ISPs can cooperatively maximize the total profit.
x62/16
x82/16
x72/4
We normalize the total required traffic load to be 1.
Backbone ISP 2

34. Problems with the current practice
Route
Topology
Global Min Cost
Well-connected topology
Topology Balkanization
MIN routing cost and MAX profit
Increased routing and reduced profit
Cost | Profit
x22/4
x12/16
x32/16
1/2
A simple example:
Two backbone ISPs
Two local ISPs
End-to-end service generates revenue
Routing costs on links
x42/8
x52/8
1/2
However, the reality is not so perfect. Selfish ISPs want to maximize their own profits by making interconnecting decisions. For example, because backbone-ISPs normally charge local ISPs for the transit services, local ISPs might not want to connect to all of them. The topology changes from a well-connected graph to be a barely connected graph. After adapting to the new topology, the achievable min-cost route becomes this. Accordingly, the routing cost increases and the profit for all the ISPs decreases. Notice that although financial transactions happen between ISPs, the aggregate profit for them is a constant sum represented by the green bar.
x62/16
x82/16
x72/4
Problem 1: ISPs interconnect selfishly to maximize profits!
e.g. Backbone ISPs charge local ISPs.

35. Problems with the current practice
Route
Topology
Global Min Cost
Hot Potato
Topology Balkanization
Increased routing and reduced profit
Cost | Profit
Profit reduction by routing inefficiency
x22/4
x12/16
1/2
A simple example:
Two backbone ISPs
Two local ISPs
End-to-end service generates revenue
Routing costs on links
x42/8
x52/8
1/2 1
Further, ISPs will maximize their own profits by choosing routing decisions. For example, the green backbone ISP can use hot-potato routing and shift all the traffic going through its own network to the peering link, making the other backbone ISP forward the traffic across the country. We can see that the routing cost for the other backbone ISP increases dramatically. As a result, this further increases the routing cost and reduces total profit.
x82/16
x72/4
Problem 1: ISPs interconnect selfishly to maximize profits!
Problem 2: ISPs route selfishly to maximize profits!
e.g. upper backbone ISP wants to use hot-potato routing to reduce its routing cost.

36. Issues of Current ISP Settlement: A Micro Perspective
Global Ideal case: cooperative ISPs
Route
Topology
Cost | Profit (maximized)
Connectivity
Global Min Cost
Well-connected
ISPs selfishly interconnect
Route
Topology
Cost | Profit (reduced)
Connectivity
Global Min Cost
Balkanized
ISPs selfishly route traffic
From a Micro-perspective, we summarize the issues as follows. Ideally, we can have a well-connected network, where ISPs fully cooperate to minimize routing costs and maximize the aggregate profit. Due to the selfish interconnecting behaviors, the topology starts to Balkanize and the total profit reduces. The connectivity is not as robust as before. Further, due to the selfish routing behaviors, the routes used by the ISPs do not minimize global routing cost. Consequently, it further reduces the aggregate profit. As a result, the Internet is not as efficient as it could be, and the selfish behaviors of the ISPs hurt the profits for themselves.
Route
Topology
Cost | Profit (further reduced)
Connectivity
Hot Potato
Balkanized
Encourage ISPs to use optimal routes and to interconnect?

37. Our solution: A clean-slate multilateral settlement
Provider ISP
Recall: three levels of ISP decisions
Interconnecting decision E
Routing decisions R
Bilateral settlements f
Multilateral settlements j
j collects revenue from customers
j distributes profits to ISPs
Settlement f affects E, R
j(E,R)
$$$ $$
Customer-Provider
Settlements
Source
Recall the three levels of ISP decisions. Because the financial settlement affects the underlying routing and interconnecting decisions, we try to solve the selfish routing and interconnecting problems by re-designing the financial settlement. We replace the current bilateral settlements with a multilateral settlement. It collects revenue from all customers and distribute to individual ISPs. We denote phi as the profit distribution mechanism, which is a function of both the interconnecting topology and the routing decisions. Under this new mechanism, ISPs make underlying interconnecting and routing decisions to maximize their profits.
Destination
Zero-Dollar
Peering
Customer ISP
Customer ISP

38. Our solution: A clean-slate multilateral settlement
Each ISP’s local interconnecting and routing decisions.
Given: j
Local decisions: Ei,Ri
Objective: to maximize ji(E,R)
From each ISP’s point of view, it knows this mechanism before it makes local interconnecting decisions Ei and local routing decisions Ri to maximize its profit phi(i). Ei Ri

39. The Shapley value mechanism j
Revenue
Profit v(S) is defined on any subset of ISPs
Profit = Revenue - Cost
Routing cost
Profit: v(S)
v( ) =0.8125
v( ) = 0.625
v( ) = 0
x22/4
Marginal contribution of ISP i to set of ISPs S: Di(S).
x12/16
x32/16
D ( ) =
v( ) -
v( ) =
1/2
Again, we consider to use the Shapley value to distribute profits. The profit function v is defined on each set of ISPs S. As we mentioned before, profit equals revenue minus cost. Suppose the set of ISPs can generate a fixed amount of revenue, they can perform a certain route with a fixed cost, then v(S) is the profit they generate. Using the previous example, if the four ISPs cooperate and use a min-cost route, the profit is maximized. Similarly, a subset of the ISPs can obtain lower profit due to a higher routing cost. If the set of ISPs are disconnected and cannot provide the service, the profit becomes zero. We further denote Delta_i(S) as the marginal contribution of ISP i to a set of ISPs S. For example, the marginal contribution of the green ISP to the set of remaining three ISPs is the profit difference between whether the green ISP is cooperating or not.
x42/8
x52/8
v( ) -
v( ) = 0.625
D ( ) =
1/2 1
x62/16
x82/16
x72/4

40. The Shapley value mechanism j
N: total # of ISPs, e.g. N=3
P: set of N! orderings
S(p,i): set of ISPs in front of ISP i p
Empty
S(p, )
D (S(p, ))
v( )=0
v( )=0.2
v( )-
v( )-
v( )=0.8
v( )=0.6
j( )=2.4/6=0.4
Then the Shapley value of an ISP can be calculated using the marginal contributions of this ISP to sets of other ISPs. N is the total number of ISPs in the system. Using an illustration of three ISPs, the summation is over the big pi, which is the set of N! orderings of the ISPs. Here we have the six different orderings of three ISPs. S(p,i) is the set of ISPs that is in front of I in the ordering pi. For example, if ISP i is the red ISP, these are the set S(pi,i). Now we can have the marginal contributions of the red ISP to the sets of ISPs. The Shapley value is the average of these N! marginal contributions.

41. Results: Routing Incentive
Route
Topology
Local Min Cost
Hot Potato
Global Min Cost j
Recall the inefficiency situation
Cost | Profit
x22/4
Shapley mechanism distributes profit
x12/16
Profit maximized
Profit increase
1/2
1/4
E.g. the upper ISP wants to minimize local routing cost
x42/8
x52/8 1
3/4
1/2
Recall the previous inefficient situation where the selfish backbone ISP uses hot-potato routing. Under our new settlement, the Shapley mechanism divides the profit to each individual ISP. Here, each color represents the profit of the ISP with the same color. Now, each ISP would consider to perform different routing strategies to maximize its own profit. For example, this backbone ISP might try to minimize the local routing costs going through its internal link and two peering links. As a result, it reduces global routing cost. After redistributing the profit, the backbone ISP’s profit is increased. Eventually, all ISPs will find that, if they perform a global min cost route, their individual profits will be maximized.
x82/16
Best strategy for all ISPs: global min cost routing
x72/4
ISPs route selfishly to maximize profits!

42. Results: Incentives for using Optimal Routes
Given any fixed interconnecting topology E, ISPs can locally decide routing strategies {Ri*} to maximize their profits.
Theorem (Incentive for routing): Any ISP i can maximize its profit ji by locally minimizing the global routing cost.
Implication: ISPs adapt to global min cost routes.
Corollary (Nash Equilibrium): Any global min cost routing decision is a Nash equilibrium for the set of all ISPs.
Implication: global min cost routes are stable.
Here, we formally state the result of incentive for using optimal routes. Assume we are given any fixed topology, we mean ISPs don’t change interconnecting decisions. Each ISP can locally choose the routes to maximize profit. Our theorem says that any ISP can maximize its profit under our profit distribution mechanism, if it uses a local route that minimizes the global routing cost. The theorem implies that whenever the current route is not optimal, each ISP has an incentive to change routes to be global optimal in order to maximize its own profit. A corollary from the theorem says, any global optimal route, which minimizes the global routing cost, is also a Nash equilibrium for all ISPs. The corollary implies that whenever the global optimal route is reached, the route is stable, because no ISP can obtain larger profit by unilaterally deviating from the optimal route. This result is quite important and surprising in the sense that the local selfish behavior coincides with the global optimal solution. In general it cannot be true. Without the mechanism, selfish behavior encourages hot-potato routing. The equilibrium is not efficient. The Shapley mechanism pulls the inefficient equilibrium to a global optimal equilibrium for all ISPs.
Surprising! Local selfish behaviors coincide with global optimal solution!

43. Results: Interconnecting Incentive
Route
Topology
Global Min Cost
Recall: the best strategy for all ISPs is to use global min cost routes. j
Cost | Profit
x22/4
Profit increase
x12/16
x32/16
5/12
1/2
E.g. the left local ISP connects to the low backbone ISP.
x42/8
x52/8
7/12
1/2
Further the right local ISP connects to the upper backbone ISP.
Let’s continue our example. Now we know, ISPs always have incentives to perform a global optimal route. Here, we assume they adapt to an optimal route whenever the topology changes. Now, ISPs start to think about using the interconnecting decisions to maximize their profits. For example, the local ISP tries to connect with the blue backbone ISP. After adapting to an optimal route, the cost reduces, and after the redistribution of profit, the profits of both connecting ISPs are increased. Similarly, the orange local ISP can also try to connect with the green backbone ISP. Now, the routing cost is minimized, and after redistributing the profit, both connecting ISPs’ profits are increased.
x62/16
x82/16
x72/4
Profit increase
ISPs interconnect selfishly to maximize profits!

44. Results: Incentive for Interconnecting
For any topology, a global optimal route R* is used by all ISPs. ISPs can locally decide interconnecting strategies {Ei*} to maximize their profits.
Theorem (Incentive for interconnecting): By interconnecting, ISPs will have non-decreasing profits.
Implication: ISPs have incentive to interconnect.
Does not mean: All pairs of ISPs should be connected.
Redundant links might not reduce routing costs.
Sunk cost is not considered.
We formalize the interconnecting incentive result here. We assume that given any topology, an optimal route will be used by all ISPs. Now ISPs can make local interconnecting decisions to change the topology. Our theorem says, whenever two ISPs interconnect, their profits are not going to decrease. Most of time, their profits are going to increase, because new link reduces global routing costs. This result implies that ISPs have incentive to interconnect under the Shapley value mechanism. However, it doesn’t mean all pairs of ISPs should be connected, because redundant links might not reduce routing costs, and the sunk cost for establishing a link is not considered.

45. Micro perspective ISP settlement: result summary
Selfishly interconnect and route
Route
Topology
Cost | Profit
Connectivity
Hot Potato
Balkanized
j solves the selfish interconnecting problem
ISPs have incentive to use optimal routes j
Route
Topology
Cost | Profit
Connectivity
Global Min Cost
Balkanized
A summary of the Micro-perspective results is the following. Current ISP practices show the selfish routing and interconnecting problems, which reduce the total profit and balkanize the topology. Under the Shapley value profit distribution mechanism, individual ISPs have incentives to use an optimal route to maximize profit for any given topology. Moreover, ISPs also have incentives to interconnect with each other to further increase their own profits. As a result, the aggregate profit can be maximized and the topology can be more robust and well-connected.
j solves the selfish routing problem
ISPs have incentive to interconnect j
Route
Topology
Cost | Profit
Connectivity
Global Min Cost
well-connected

46. My research: thesis work

47. My research: past work

48. My research: future interests
Modeling: Optimization, Stochastic Process
Distributed Systems and Networks
Implementation: Algorithm, Protocol Design, Stability and Convergence Analysis
Algorithmic Game Theory
Algorithmic Mechanism Design
Network Economics
Mechanism Design: e.g. Tax Policy, Auction Theory
Non-cooperative Game Theory
Micro-economics and Game Theory
Coalition Game Theory