Interdomain routing and games

We present a game-theoretic model that captures many of the intricacies of \emph{interdomain routing} in today's Internet. In this model, the strategic agents are source nodes located on a network, who aim to send traffic to a unique destination node. The interaction between the agents is dynamic and complex -- asynchronous, sequential, and based on partial information. Best-reply dynamics in this model capture crucial aspects of the only interdomain routing protocol de facto, namely the Border Gateway Protocol (BGP). We study complexity and incentive-related issues in this model. Our main results are showing that in realistic and well-studied settings, BGP is incentive-compatible. I.e., not only does myopic behaviour of all players \emph{converge} to a ``stable'' routing outcome, but no player has motivation to unilaterally deviate from the protocol. Moreover, we show that even \emph{coalitions} of players of \emph{any} size cannot improve their routing outcomes by collaborating. Unlike the vast majority of works in mechanism design, our results do not require any monetary transfers (to or by the agents).Interdomain Routing; Network Games; BGP protocol;

A distributed auction-based algorithm to allocate bandwidth over paths

Session 01 : Scheduling and bandwidth allocationInternational audienceIn the literature, Vickrey-Clark-Groves (VCG) double-sided auctions have been applied to inter-domain traffic exchange because they provide incentives to be truthful and lead to an efficient use of the network, among relevant properties of mechanism design. Unfortunately, the resulting resource allocation scheme is neither budget-balanced nor solvable in a decentralized way, two important properties. We present a different but more realistic auction-based algorithm for allocating bandwidth over paths to end users or ISPs, leading to a new budget-balanced pricing scheme for which allocations and charges can be computed in a decentralized way

Identifier-Based Discovery in Large-Scale Networks

The design of any network mechanism that requires collaboration among selfish agents could only benefit from accounting for the complex social and economic interactions and incentives of the agents using the design. This chapter presents a broad treatment of the main economic issues that arise in the context of identifier-based discovery on large scale networks, particularly on the Internet. An “identified” object (such as a node or service), referred to as a player, demands to be discoverable by the rest of the network on its “identifier”. A discovery scheme provides such a service to the players and incurs a cost for doing so. Providing such a service while accounting for the cost and making sure that the incentives of the players are aligned is the general economic problem that we address in this work. After introducing the identifier-based discovery problem, we present a taxonomy of discovery schemes and proposals based on their business model and we pose several questions that are becoming increasingly important as we proceed to design the inter-network of the future. An incentive model for distributed discovery in the context of the Border Gateway Protocol (BGP) and path-vector protocols in general is then presented. We model BGP route distribution and computation using a game in which a BGP speaker advertises its prefix to its direct neighbors promising them a reward for further distributing the route deeper into the network. The neighbors do the same thing with their direct neighbors, and so on. The result of this cascaded route distribution is a globally advertised prefix and hence discoverability. We present initial results on the existence of equilibria in the game and we motivate our ongoing work

Auction-based schemes for multipath routing in selfish networks

We study multipath routing with traffic assignment in selfish networks. Based on the Vickrey-Clarke-Groves (VCG) auction, an optimal and strategy-proof scheme, known as optimal auction-based multipath routing (OAMR), is developed. However, OAMR is computationally expensive and cannot run in real time when the network size is large. Therefore, we propose sequential auction-based multipath routing (SAMR). SAMR handles routing requests sequentially using some greedy strategies. In particular, with reference to the Ausubel auction, we develop a water-draining algorithm to assign the traffic of a request among its available paths and determine the payment of the transmission in approximately constant time. Our simulation results show that SAMR can rapidly compute the allocations and payments of requests with small sacrifice on the system cost. Moreover, various sequencing strategies for sequential auction are also investigated. © 2013 IEEE.published_or_final_versio

Computing With Distributed Information

The age of computing with massive data sets is highlighting new computational challenges. Nowadays, a typical server may not be able to store an entire data set, and thus data is often partitioned and stored on multiple servers in a distributed manner. A natural way of computing with such distributed data is to use distributed algorithms: these are algorithms where the participating parties (i.e., the servers holding portions of the data) collaboratively compute a function over the entire data set by sending (preferably small-size) messages to each other, where the computation performed at each participating party only relies on the data possessed by it and the messages received by it. We study distributed algorithms focused on two key themes: convergence time and data summarization. Convergence time measures how quickly a distributed algorithm settles on a globally stable solution, and data summarization is the approach of creating a compact summary of the input data while retaining key information. The latter often leads to more efficient computation and communication. The main focus of this dissertation is on design and analysis of distributed algorithms for important problems in diverse application domains centering on the themes of convergence time and data summarization. Some of the problems we study include convergence time of double oral auction and interdomain routing, summarizing graphs for large-scale matching problems, and summarizing data for query processing