4,688 research outputs found
Implications of Selfish Neighbor Selection in Overlay Networks
In a typical overlay network for routing or content sharing, each node must select a fixed number of immediate overlay neighbors for routing traffic or content queries. A selfish node entering such a network would select neighbors so as to minimize the weighted sum of expected access costs to all its destinations. Previous work on selfish neighbor selection has built intuition with simple models where edges are undirected, access costs are modeled by hop-counts, and nodes have potentially unbounded degrees. However, in practice, important constraints not captured by these models lead to richer games with substantively and fundamentally different outcomes. Our work models neighbor selection as a game involving directed links, constraints on the number of allowed neighbors, and costs reflecting both network latency and node preference. We express a node's "best response" wiring strategy as a k-median problem on asymmetric distance, and use this formulation to obtain pure Nash equilibria. We experimentally examine the properties of such stable wirings on synthetic topologies, as well as on real topologies and maps constructed from PlanetLab and AS-level Internet measurements. Our results indicate that selfish nodes can reap substantial performance benefits when connecting to overlay networks composed of non-selfish nodes. On the other hand, in overlays that are dominated by selfish nodes, the resulting stable wirings are optimized to such great extent that even non-selfish newcomers can extract near-optimal performance through naive wiring strategies.Marie Curie Outgoing International Fellowship of the EU (MOIF-CT-2005-007230); National Science Foundation (CNS Cybertrust 0524477, CNS NeTS 0520166, CNS ITR 0205294, EIA RI 020206
The Social Medium Selection Game
We consider in this paper competition of content creators in routing their
content through various media. The routing decisions may correspond to the
selection of a social network (e.g. twitter versus facebook or linkedin) or of
a group within a given social network. The utility for a player to send its
content to some medium is given as the difference between the dissemination
utility at this medium and some transmission cost. We model this game as a
congestion game and compute the pure potential of the game. In contrast to the
continuous case, we show that there may be various equilibria. We show that the
potential is M-concave which allows us to characterize the equilibria and to
propose an algorithm for computing it. We then give a learning mechanism which
allow us to give an efficient algorithm to determine an equilibrium. We finally
determine the asymptotic form of the equilibrium and discuss the implications
on the social medium selection problem
Approximating Generalized Network Design under (Dis)economies of Scale with Applications to Energy Efficiency
In a generalized network design (GND) problem, a set of resources are
assigned to multiple communication requests. Each request contributes its
weight to the resources it uses and the total load on a resource is then
translated to the cost it incurs via a resource specific cost function. For
example, a request may be to establish a virtual circuit, thus contributing to
the load on each edge in the circuit. Motivated by energy efficiency
applications, recently, there is a growing interest in GND using cost functions
that exhibit (dis)economies of scale ((D)oS), namely, cost functions that
appear subadditive for small loads and superadditive for larger loads.
The current paper advances the existing literature on approximation
algorithms for GND problems with (D)oS cost functions in various aspects: (1)
we present a generic approximation framework that yields approximation results
for a much wider family of requests in both directed and undirected graphs; (2)
our framework allows for unrelated weights, thus providing the first
non-trivial approximation for the problem of scheduling unrelated parallel
machines with (D)oS cost functions; (3) our framework is fully combinatorial
and runs in strongly polynomial time; (4) the family of (D)oS cost functions
considered in the current paper is more general than the one considered in the
existing literature, providing a more accurate abstraction for practical energy
conservation scenarios; and (5) we obtain the first approximation ratio for GND
with (D)oS cost functions that depends only on the parameters of the resources'
technology and does not grow with the number of resources, the number of
requests, or their weights. The design of our framework relies heavily on
Roughgarden's smoothness toolbox (JACM 2015), thus demonstrating the possible
usefulness of this toolbox in the area of approximation algorithms.Comment: 39 pages, 1 figure. An extended abstract of this paper is to appear
in the 50th Annual ACM Symposium on the Theory of Computing (STOC 2018
Connectivity and equilibrium in random games
We study how the structure of the interaction graph of a game affects the
existence of pure Nash equilibria. In particular, for a fixed interaction
graph, we are interested in whether there are pure Nash equilibria arising when
random utility tables are assigned to the players. We provide conditions for
the structure of the graph under which equilibria are likely to exist and
complementary conditions which make the existence of equilibria highly
unlikely. Our results have immediate implications for many deterministic graphs
and generalize known results for random games on the complete graph. In
particular, our results imply that the probability that bounded degree graphs
have pure Nash equilibria is exponentially small in the size of the graph and
yield a simple algorithm that finds small nonexistence certificates for a large
family of graphs. Then we show that in any strongly connected graph of n
vertices with expansion the distribution of the number
of equilibria approaches the Poisson distribution with parameter 1,
asymptotically as .Comment: Published in at http://dx.doi.org/10.1214/10-AAP715 the Annals of
Applied Probability (http://www.imstat.org/aap/) by the Institute of
Mathematical Statistics (http://www.imstat.org
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