Performance Analysis of CHOKe with Multiple UDP Flows
ABSTRACT Several active queue management schemes have been proposed to provide the fairness among flows. In particular, CHOKe is stateless and simple to implement, but it can effectively penalize UDP flows which usually obtain more bandwidth than TCP flows. However, its performance has not been analyzed in terms of the UDP throughput and the Jain's fairness index in the extended case of multiple UDP flows. In this paper, we derive the UDP throughput and Jain's fairness index under CHOKe. The simulation results illustrate the accuracy of our analysis and verify our derived result
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ABSTRACT: The growing pervasiveness of the internet has created a new class of algorithmic problems: those in which the strategic interaction of autonomous, self-interested entities must be accounted for. So motivated, we seek to (1) use game theoretic models and techniques to study practical problems in load balancing, data streams and internet traffic congestion, and (2) demonstrate the usefulness of evolutionary game theory’s adaptive model as an analytical and evaluative tool. First we consider the evolutionary game theory concept of stochastic stability, and propose the price of stochastic anarchy as an alternative to the price of anarchy for quantifying the cost of having no central authority. Unlike Nash equilibria, stochastically stable states are the result of natural dynamics of large populations of computationally bounded agents, and are resilient to small perturbations from ideal play. To illustrate the utility of stochastic stability, we study the load balancing game on related machines, which has an unbounded price of anarchy, even in the case of two jobs and two machines. We show that in contrast, even in the general case, the price of stochastic anarchy is bounded. Next, we propose auction-based mechanisms for admission control of continuous queries to a Data Stream Management System. When submitting a query, each user also submits a bid: how much she is willing to pay for her query to run. Our mechanisms must admit queries and set payments in a way that maximizes system revenue while incentivizing customers to use the system honestly. We propose several manipulation-resistant payment mechanisms and prove that one guarantees a profit close to a standard profit benchmark, and the others perform well experimentally. Finally, we study the long standing problem of congestion control at bottleneck routers on the internet. We examine the effectiveness of commonly-used queuing policies when each network endpoint is self-interested and has no information about the other endpoints’ actions or preferences. By employing evolutionary game theory, we find that while bottleneck routers face heavy congestion at stochastically stable states under policies being currently deployed, a practical policy that was recently proposed yields fair and efficient conditions with no congestion.
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ABSTRACT: 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.  to find the stochastically stable states: the states of the system that will be reached in the long run.
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ABSTRACT: Congestion control in internet routers while providing fairness among flows is a challenging task. Malicious flows such as UDP flows tend to occupy most of the buffer space leaving little or no room for TCP flows which have their own congestion control mechanism when congestion occurs. In this paper, we develop a modified CHOKe algorithm based on optimizing two main parameters; number of drop candidates (m) chosen for comparison with the incoming packet and the buffer location of those drop candidates (l) in order to improve the fairness. The simulation results show that the modified CHOKe has better performance compared to the original CHOKe, Random Early Detection (RED) and Drop Tail queue management mechanisms.3rd International Congress on Ultra Modern Telecommunications and Control Systems and Workshops, ICUMT 2011, Budapest, Hungary, October 5-7, 2011; 01/2011