TY - JOUR
T1 - Non-cooperative multicast and facility location games
AU - Chekuri, Chandra
AU - Chuzhoy, Julia
AU - Lewin-Eytan, Liane
AU - Naor, Joseph
AU - Orda, Ariel
N1 - Funding Information:
Manuscript received July 1, 2006; revised February 15, 2006. Chandra Chekuri is with the Dept. of Computer Science, Univ. of Illinois, Urbana, IL 61801. This work was done while the author was at Lucent Bell Labs. (e-mail: [email protected]). Julia Chuzhoy is with the School of Mathematics, Institute for Advanced Study, Princeton NJ 08540 (e-mail: [email protected]). Liane Lewin-Eytan is with the Department of Electrical Engineering, Technion, Haifa 32000, Israe (e-mail: [email protected]). Joseph (Seffi) Naor is with the Computer Science Department, Technion, Haifa 32000, Israel. Part of this research was done while visiting Microsoft Research, Redmond, WA 98052. This research is supported in part by US-Israel BSF Grant 2002276 (e-mail: [email protected]). Ariel Orda is with the Department of Electrical Engineering, Technion, Haifa 32000, Israel (e-mail: [email protected]). Digital Object Identifier 10.1109/JSAC.2007.070813.
PY - 2007/8
Y1 - 2007/8
N2 - We consider a multicast game with selfish noncooperative players. There is a special source node and each player is interested in connecting to the source by making a routing decision that minimizes its payment. The mutual influence of the players is determined by a cost sharing mechanism, which in our case evenly splits the cost of an edge among the players using it. We consider two different models: an integral model, where each player connects to the source by choosing a single path, and a fractional model, where a player is allowed to split the flow it receives from the source between several paths. In both models we explore the overhead incurred in network cost due to the selfish behavior of the users, as well as the computational complexity of finding a Nash equilibrium. The existence of a Nash equilibrium for the integral model was previously established by the means of a potential function. We prove that finding a Nash equilibrium that minimizes the potential function is NP-hard. We focus on the price of anarchy of a Nash equilibrium resulting from the best-response dynamics of a game course, where the players join the game sequentially. For a game with n players, we establish an upper bound of 0( 7rafic;n log 2n) on the price of anarchy, and a lower bound of Ω(log n/ log log n). For the fractional model, we prove the existence of a Nash equilibrium via a potential function and give a polynomial time algorithm for computing an equilibrium that minimizes the potential function. Finally, we consider a weighted extension of the multicast game, and prove that in the fractional model, the game always has a Nash equilibrium.
AB - We consider a multicast game with selfish noncooperative players. There is a special source node and each player is interested in connecting to the source by making a routing decision that minimizes its payment. The mutual influence of the players is determined by a cost sharing mechanism, which in our case evenly splits the cost of an edge among the players using it. We consider two different models: an integral model, where each player connects to the source by choosing a single path, and a fractional model, where a player is allowed to split the flow it receives from the source between several paths. In both models we explore the overhead incurred in network cost due to the selfish behavior of the users, as well as the computational complexity of finding a Nash equilibrium. The existence of a Nash equilibrium for the integral model was previously established by the means of a potential function. We prove that finding a Nash equilibrium that minimizes the potential function is NP-hard. We focus on the price of anarchy of a Nash equilibrium resulting from the best-response dynamics of a game course, where the players join the game sequentially. For a game with n players, we establish an upper bound of 0( 7rafic;n log 2n) on the price of anarchy, and a lower bound of Ω(log n/ log log n). For the fractional model, we prove the existence of a Nash equilibrium via a potential function and give a polynomial time algorithm for computing an equilibrium that minimizes the potential function. Finally, we consider a weighted extension of the multicast game, and prove that in the fractional model, the game always has a Nash equilibrium.
KW - Multicast game
KW - Nash equilibrium
KW - Price of anarchy
KW - Price of stability
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U2 - 10.1109/JSAC.2007.070813
DO - 10.1109/JSAC.2007.070813
M3 - Article
AN - SCOPUS:34547538270
SN - 0733-8716
VL - 25
SP - 1193
EP - 1206
JO - IEEE Journal on Selected Areas in Communications
JF - IEEE Journal on Selected Areas in Communications
IS - 6
ER -