Congestion avoidance and control
SIGCOMM '88 Symposium proceedings on Communications architectures and protocols
Analysis and simulation of a fair queueing algorithm
SIGCOMM '89 Symposium proceedings on Communications architectures & protocols
Random early detection gateways for congestion avoidance
IEEE/ACM Transactions on Networking (TON)
Making greed work in networks: a game-theoretic analysis of switch service disciplines
IEEE/ACM Transactions on Networking (TON)
Proceedings of the ACM SIGCOMM '98 conference on Applications, technologies, architectures, and protocols for computer communication
A game-theoretic approach towards congestion control in communication networks
ACM SIGCOMM Computer Communication Review
Selfish behavior and stability of the internet:: a game-theoretic analysis of TCP
Proceedings of the 2002 conference on Applications, technologies, architectures, and protocols for computer communications
Fair and efficient router congestion control
SODA '04 Proceedings of the fifteenth annual ACM-SIAM symposium on Discrete algorithms
Understanding CHOKe: throughput and spatial characteristics
IEEE/ACM Transactions on Networking (TON)
An evolutionary game-theoretic approach to congestion control
Performance Evaluation - Performance 2005
Window-Games between TCP Flows
SAGT '08 Proceedings of the 1st International Symposium on Algorithmic Game Theory
The Price of Stochastic Anarchy
SAGT '08 Proceedings of the 1st International Symposium on Algorithmic Game Theory
Game-Theoretic analysis of internet switching with selfish users
WINE'05 Proceedings of the First international conference on Internet and Network Economics
Sponsored search auctions: an overview of research with emphasis on game theoretic aspects
Electronic Commerce Research
| Hi-index | 0.00 |
Congestion control at bottleneck routers on the internet is a long standing problem. Many policies have been proposed for effective ways to drop packets from the queues of these routers so that network endpoints will be inclined to share router capacity fairly and minimize the overflow of packets trying to enter the queues. We study just how effective some of these queuing policies are when each network endpoint is a self-interested player with no information about the other players' actions or preferences. By employing the adaptive learning model of evolutionary game theory, we study policies such as Droptail, RED, and the greedy-flow-punishing policy proposed by Gao et al. [10] to find the stochastically stable states: the states of the system that will be reached in the long run.