Network Design with Coverage Costs

We study network design with a cost structure motivated by redundancy in data traffic. We are given a graph, g groups of terminals, and a universe of data packets. Each group of terminals desires a subset of the packets from its respective source. The cost of routing traffic on any edge in the network is proportional to the total size of the distinct packets that the edge carries. Our goal is to find a minimum cost routing. We focus on two settings. In the first, the collection of packet sets desired by source-sink pairs is laminar. For this setting, we present a primal-dual based 2-approximation, improving upon a logarithmic approximation due to Barman and Chawla (2012). In the second setting, packet sets can have non-trivial intersection. We focus on the case where each packet is desired by either a single terminal group or by all of the groups, and the graph is unweighted. For this setting we present an O(log g)-approximation. Our approximation for the second setting is based on a novel spanner-type construction in unweighted graphs that, given a collection of g vertex subsets, finds a subgraph of cost only a constant factor more than the minimum spanning tree of the graph, such that every subset in the collection has a Steiner tree in the subgraph of cost at most O(log g) that of its minimum Steiner tree in the original graph. We call such a subgraph a group spanner.Comment: Updated version with additional result

Dynamic Policies for Cooperative Networked Systems

A set of economic entities embedded in a network graph collaborate by opportunistically exchanging their resources to satisfy their dynamically generated needs. Under what conditions their collaboration leads to a sustainable economy? Which online policy can ensure a feasible resource exchange point will be attained, and what information is needed to implement it? Furthermore, assuming there are different resources and the entities have diverse production capabilities, which production policy each entity should employ in order to maximize the economy's sustainability? Importantly, can we design such policies that are also incentive compatible even when there is no a priori information about the entities' needs? We introduce a dynamic production scheduling and resource exchange model to capture this fundamental problem and provide answers to the above questions. Applications range from infrastructure sharing, trade and organisation management, to social networks and sharing economy services.Comment: 6-page version appeared at ACM NetEcon' 1

On our Knowledge of Markets for Knowledge ― A Survey

At the Lisbon Summit 2000 the EU set herself the goal of transforming the European Union by 2010 into “the most competitive and dynamic knowledge based economy in the world capable of sustainable economic growth with more and better jobs and greater social cohesion”. I take this statement as a starting point for this paper for two reasons: On the one hand it acknowledges the crucial role of knowledge in an advanced economy. On the other hand, it raises the question what needs to be done in order to achieve this ambitious goal. In particular, since the EU is also committed to a market economy and the maintenance of competition the question arises how well markets function with respect to the creation and distribution of knowledge, and what measures may be required, either to support the market mechanism or to replace it by some other institutions. This article deals with the ?rst question and offers a survey of the problems encountered in markets dealing with knowledge. In the next section I discuss briefly the role of knowledge and information in economics. After that I point out a few difficulties with finding a precise and generally accepted definition of knowledge. Section 4 is the core of the article and discusses various types of market failures which might occur when the commodity produced and traded is knowledge. I conclude with a few suggestions for further research

Game-theoretic Resource Allocation Methods for Device-to-Device (D2D) Communication

Device-to-device (D2D) communication underlaying cellular networks allows mobile devices such as smartphones and tablets to use the licensed spectrum allocated to cellular services for direct peer-to-peer transmission. D2D communication can use either one-hop transmission (i.e., in D2D direct communication) or multi-hop cluster-based transmission (i.e., in D2D local area networks). The D2D devices can compete or cooperate with each other to reuse the radio resources in D2D networks. Therefore, resource allocation and access for D2D communication can be treated as games. The theories behind these games provide a variety of mathematical tools to effectively model and analyze the individual or group behaviors of D2D users. In addition, game models can provide distributed solutions to the resource allocation problems for D2D communication. The aim of this article is to demonstrate the applications of game-theoretic models to study the radio resource allocation issues in D2D communication. The article also outlines several key open research directions.Comment: Accepted. IEEE Wireless Comms Mag. 201

Application of Market Models to Network Equilibrium Problems

We present a general two-side market model with divisible commodities and price functions of participants. A general existence result on unbounded sets is obtained from its variational inequality re-formulation. We describe an extension of the network flow equilibrium problem with elastic demands and a new equilibrium type model for resource allocation problems in wireless communication networks, which appear to be particular cases of the general market model. This enables us to obtain new existence results for these models as some adjustments of that for the market model. Under certain additional conditions the general market model can be reduced to a decomposable optimization problem where the goal function is the sum of two functions and one of them is convex separable, whereas the feasible set is the corresponding Cartesian product. We discuss some versions of the partial linearization method, which can be applied to these network equilibrium problems.Comment: 18 pages, 3 table