Hyeryung Jang

On the stability of ISPs' coalition structure: Shapley value based revenue sharing

The Internet is a complex system, consisting of different economic players in terms of access/transit connection and content distribution, which are typically selfish and try to maximize their own profits. Due to this different perspective of economic interest as well as dynamic changes of the Internet market, a certain degree of techno-economic inefficiency has naturally been observed, e.g., unstable peering and revenue imbalance among content, eyeball, and transit ISPs (Internet Service Providers). At the center of this issue is “good” revenue sharing among them. Recently, revenue sharing based on the notion of Shapley Value (SV) from cooperative game theory has been applied to address the afore-mentioned issue, shedding light upon many nice properties which have been used not only to understand the current Internet eco-system but also to predict its future. However, the positive features from the SV based revenue sharing can be practically feasible only when the providers agree to form a grand coalition, which may not hold in practice. In this paper, we first investigate the conditions under which the grand coalition is stable under SV by classifying the network into two cases: under-demanded and over-demanded. We then study the gap between the conditions of the grand coalitions stability and optimal coalition structures (i.e., coalition structures that maximize the aggregate revenue of ISPs).

Traffic Scheduling and Revenue Distribution Among Providers in the Internet: Tradeoffs and Impacts

The Internet consists of economically selfish players in terms of access/transit connection and content distribution. Such selfish behaviors often lead to techno-economic inefficiencies, such as unstable peering and revenue imbalance. Recent research results suggest that cooperation-based fair revenue sharing, i.e., multi-level Internet service provider (ISP) settlements, can be a candidate solution to avoid unfair revenue share. However, it has been under-explored whether selfish ISPs actually cooperate or not (often referred to as the stability of coalition), because they may partially cooperate or even do not cooperate, depending on how much revenue is distributed to each individual ISP. In this paper, we study this stability of coalition in the Internet, where our aim is to investigate the conditions under which ISPs cooperate under different regimes on the traffic demand and network bandwidth. We first consider the under-demanded regime, i.e., network bandwidth exceeds traffic demand, where revenue sharing based on Shapley value leads ISPs to entirely cooperate, i.e., stability of the grand coalition. Next, we consider the over-demanded regime, i.e., traffic demand exceeds network bandwidth, where there may exist some ISPs who deviate from the grand coalition. In particular, this deviation depends on how users’ traffic is handled inside the network, for which we consider three traffic scheduling policies having various degrees of content-value preference. We analytically compare those three scheduling policies in terms of network neutrality, and stability of cooperation that provides useful implications on when and how multi-level ISP settlements help and how the Internet should be operated for stable peering and revenue balance among ISPs.

Game Theoretic Perspective of Optimal CSMA

Game-theoretic approaches have provided valuable insights into the design of robust local control rules for the individuals in multi-agent systems, e.g., Internet congestion control, road transportation networks, and so on. In this paper, we introduce a non-cooperative medium access control game for wireless networks and propose new fully distributed carrier sense multiple access (CSMA) algorithms that are provably optimal in the sense that their long-term throughputs converge to the optimal solution of a utility maximization problem over the maximum throughput region. The most significant part of our approach lies in introducing novel price functions in agents’ utilities so that the proposed game admits an ordinal potential function with no price-of-anarchy. The game formulation naturally leads to game-based dynamics finding a Nash equilibrium, but they often require global information. Toward our goal of designing fully distributed operations, we propose new game-inspired dynamics by utilizing a certain property of CSMA that enables links to estimate their temporary throughputs without message passing. They can be thought of as stochastic approximations to the standard dynamics, which is a new feature in our work, not prevalent in other traditional game-theoretic approaches. We show that they converge to a Nash equilibrium, and numerically evaluate their performance to support our theoretical findings.

Distributed coordination maximization over networks: a stochastic approximation approach

In various online/offline networked environments, it is very popular that the system can benefit from coordinating actions of two interacting nodes, but incur some cost due to such coordination. Examples include a wireless sensor networks with duty cycling, where a sensor node consumes a certain amount of energy when it is awake, but a coordinated operation of sensors enables some meaningful tasks, e.g., sensed data forwarding, collaborative sensing of a phenomenon, or efficient decision of further sensing actions. In this paper, we formulate an optimization problem that captures the amount of coordination gain at the cost of node activation over networks. This problem is challenging since the target utility is a function of the long-term time portion of the inter-coupled activations of two adjacent nodes, and thus a standard Lagrange duality theory is hard to apply to obtain a distributed decomposition as in the standard NUM (Network Utility Maximization). We propose a fully-distributed algorithm that requires only one-hop message passing. Our approach is inspired by a control of Ising model in statistical physics, and the proposed algorithm is motivated by a stochastic approximation method that runs a Markov chain incompletely over time, but provably guarantees its convergence to the optimal solution. We validate our theoretical findings on convergence and optimality through extensive simulations under various scenarios.

Network traffic reduction through smart network

To cope with exploding Internet traffic, telecommunication companies are considering smart network technology. In this paper, we try to evaluate the impact of the smart network to the Internet traffic. First, ISP utilizes nodes smartly so that they can do smart caching. Second, ISP controls both routing and traffic matrix as a Telco-CDN so that they can decide more efficient traffic routing policy. Third, ISPs cooperate with each other so that they efficiently control IX traffic between them as a Telco-CDNi. Finally we estimate economic value of smart network from the traffic reduction of these points.

Learning Data Dependency with Communication Cost

In this paper, we consider the problem of recovering a graph that represents the statistical data dependency among nodes for a set of data samples generated by nodes, which provides the basic structure to perform an inference task, such as MAP (maximum a posteriori). This problem is referred to as structure learning. When nodes are spatially separated in different locations, running an inference algorithm requires a non-negligible amount of message passing, incurring some communication cost. We inevitably have the trade-off between the accuracy of structure learning and the cost we need to pay to perform a given message-passing based inference task because the learnt edge structures of data dependency and physical connectivity graph are often highly different. In this paper, we formalize this trade-off in an optimization problem which outputs the data dependency graph that jointly considers learning accuracy and message-passing cost. We focus on a distributed MAP as the target inference task due to its popularity, and consider two different implementations, ASYNC-MAP and SYNC-MAP that have different message-passing mechanisms and thus different cost structures. In ASYNC-MAP, we propose a polynomial time learning algorithm that is optimal, motivated by the problem of finding a maximum weight spanning tree. In SYNC-MAP, we first prove that it is NP-hard and propose a greedy heuristic. For both implementations, we then quantify how the probability that the resulting data graphs from those learning algorithms differ from the ideal data graph decays as the number of data samples grows, using the large deviation principle, where the decaying rate is characterized by some topological structures of both original data dependency and physical connectivity graphs as well as the degree of the trade-off, which provides some guideline on how many samples are necessary to obtain a certain learning accuracy. We validate our theoretical findings through extensive simulations, which confirm that it has a good match.

Adiabatic Persistent Contrastive Divergence learning

This paper studies the problem of parameter learning in graphical models having latent variables, where the standard approach is the expectation maximization algorithm alternating expectation (E) and maximization (M) steps. However, both E and M steps are computationally intractable for high dimensional data, while the substitution of one step to a faster surrogate for combating against intractability can often cause failure in convergence. To tackle the issue, the Contrastive Divergence (CD) learning scheme has been popularly used in the deep learning community, where it runs the mean-field approximation in E step and a few cycles of Markov Chains (MC) in M step. In this paper, we analyze a variant of CD, called Adiabatic Persistent Contrastive Divergence (APCD), which runs a few cycles of MCs in both E and M steps. Using multi-time-scale stochastic approximation theory, we prove that APCD converges to a correct optimum, where the standard CD is impossible to have such a guarantee due to the mean-field approximation gap in E step. Despite of such stronger theoretical guarantee of APCD, its possible drawback is on slow mixing on E step for practical purposes. To address the issue, we also design a hybrid approach applying both mean-field and MC approximations in E step, where it outperforms the standard mean-field-based CD in our experiments on real-world datasets.

On the interaction between content-oriented traffic scheduling and revenue sharing among providers

The Internet consists of economically selfish players in terms of access/transit connection, content distribution, and users. Such selfish behaviors often lead to techno-economic inefficiencies such as unstable peering and revenue imbalance. Recent research results suggest that cooperation in revenue sharing (thus multi-level ISP settlements) can be a candidate solution for the problem of unfair revenue share. However, it is unclear whether providers are willing to behave cooperatively. In this paper, we study the interaction between how content-oriented traffic scheduling at the edge is and how stable the intended cooperation is. We consider three traffic scheduling policies having various degrees of content-value preference, compare them in terms of implementation complexity, network neutrality, and stability of cooperation, and present interesting trade-offs among them.