E. Altman and T. Bas ar, "Multi-user rate-based flow control," IEEE Trans. Commun., vol. 46, no. 7, pp. 940--949, Jul. 1998.  Home/Search   Document Details and Download   Summary   Related Articles   Check

....P n(t h) j=1 s j (t) if n(t h) 0 0 otherwise. 5) Writing the buffer occupancy level (called the buffer level henceforth) at time t as q(t) 2 R, we model the buffer dynamics using a first order differential equation: dt q(t) u(t) v(t) 6) As in many other works on buffer control (e.g. [9], 10] we ignored buffer boundaries for tractability of the problem. Combining (3) and (6) we have the following system: dq(t) dv(t) dw(t) 5 = 0 1 0 0 0 1 5 2 v(t) 5 dt 0 5 u(t)dt 0 b 1 0 5 2 v(t) 5 dN(t) 7) where the variable w(t) is created to make the equation ....

E. Altman and T. Basar, "Multiuser rate-based flow control," IEEE Trans. Commun., vol. 46, no. 7, Jul. 1998, pp. 940--949.

.... better performance by incorporating control theoretic techniques, including proportional derivative (PD) controllers [9] 10] 17] 30] and those using optimal control and dynamic game techniques such as linear quadratic (LQ) team, H 1 , and noncooperative game controllers [12] 13] [11]. We motivate our work by noticing that most of the above control schemes are designed for constant or slowly varying service rates (with the exception of the LQ team and the H 1 controllers, which do consider short term variation in the service rate) These controllers aim to balance ....

E. Altman and T. Basar, "Multiuser rate-based flow control," IEEE Trans. Commun., vol. 46, no. 7, Jul. 1998.

.... several control theoretic approaches have been studied, including proportional derivative (PD) controllers and their variants [12] 13] 20] and those using optimal control and dynamic game techniques such as linear quadratic (LQ) team, H 1 , and noncooperative game controllers [15] 16] [14]. We motivate our work by noticing that most of the above control schemes are designed for constant or slowlyvarying service rates (with the exception of the LQ team and the H 1 controllers, which do consider short term variation in the service rate) We call these connectionlevel congestion ....

....(s 0 ) Gamma a( x) which represents the service rate at state s 0 minus the aggregate arrival rate from the sources. Then, the queue length component of the state changes according to the following difference equation, commonly called Lindley s equation (see also, e.g. 12] INFOCOM 2001 4 [14]) l 0 = maxfminfl Gamma c 0 T ; Bg; 0g: 1) Finally, the control history updates as follows: u 0 Gamma1 = u; u 0 i = u i 1 ; i = Gamma2; Gammad max : Reward Structure. We define the one step reward at state x by R( x) F ( x) Gamma ffD( x) where ff 0, F ( x) ....

E. Altman and T. Basar, "Multiuser rate-based flow control," IEEE Trans. Commun., vol. 46, no. 7, Jul. 1998.

....groups are transient and short lived. Furthermore, it is the profit seeking supplier who bears the operating cost of the multicast infrastructure. Game theory has also been used in less traditional and more technical problem domains, such as routing [LO97] KVA98] KO99] flow control [KL95] AB98] and congestion control [She95] In these works the decentralized control of networks is modeled as a game between noncooperating and selfish users, each trying to optimize its own subjective goal. The focal points of this research are showing convergence to equilibrium point(s) showing the ....

E. Altman and T. Basar. Multiuser rate-based flow control. IEEE Transactions on Communications, 46(7):940 -- 949, July 1998.

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E. Altman and T. Bas ar, "Multi-user rate-based flow control," IEEE Trans. Commun., vol. 46, no. 7, pp. 940--949, Jul. 1998.

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E. Altman and T. Basar, "Multi-user rate-based flow control," IEEE Transactions on Communications, vol. 46(7), pp. 940--949, july 1998.

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E. Altman and T. Basar, "Multi-user rate-based flow control," IEEE Transactions on Communications, vol. 46(7), pp. 940--949, July 1998.

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E. Altman and T. Basar, "Multi-user rate-based flow control," IEEE Transactions on Communications, vol. 46(7), pp. 940--949, july 1998.

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E. Altman and T. Basar, "Multi-user rate-based flow control," IEEE Transactions on Communications, vol. 46(7), pp. 940--949, july 1998.

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E. Altman and T. Basar, "Multi-user rate-based flow control," IEEE Transactions on Communications, vol. 46(7), pp. 940--949, july 1998.

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Altman E,Basar T. Multi-user rate-based flow control. IEEE Transactions on Communications 1998; 940--9.

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E. Altman and T. Basar, "Multi-User Rate-Based Flow Control," IEEE Transactions on Communications, vol. 46(7), pp. 940--949, July 1998.

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E. Altman and T. Basar, "Multi-user rate-based flow control," IEEE Transactions on Communications, vol. 46(7), pp. 940--949, july 1998.

....analysis. Modeling communication network problems as dynamic games has produced Nash equilibria solutions in many settings such as capacity allocation in routing [7] congestion control in product form networks [8] ow control in Markovian queueing networks [9] and rate based ow control [1]. To reach Nash equilibria in decentralized settings, necessitated by the fact that users do not have access to other agents bidding strategies and utilities, iterative algorithms based on network feedback that converge to a stable operating point are often necessary. Game theoretic perspectives ....

....iterative algorithms based on network feedback that converge to a stable operating point are often necessary. Game theoretic perspectives have resulted in existence of Nash equilibria in multiclass trac environments, and the convergence conditions of various algorithms have been investigated [1], 4] In this paper, we attempt to integrate market based modeling and game theory to introduce a new market mechanism to manage network resources. We show that a unique Nash equilibrium with certain pro t maximization properties exist, and investigate decentralized methods to reach this ....

E. Altman and T. Basar. \Multi-User RateBased Flow Control," in IEEE Transactions on Communications, vol. 46, pp. 940-949, July 1998.

....congestion control game is that of Nash equilibrium [2] where each player minimizes his her own cost (or maximize payoff) given all other players strategies. There is rich literature on game theoretic analysis of flow control problems utilizing both cooperative [3] and noncooperative [4] [5], 6] frameworks. Congestion control schemes utilizing pricing schemes based on explicit feedback have been proposed by Kelly et al. 7] 8] and Gibbens et al. 9] and subsequent studies have further elaborated on this approach following its basic principles [10] 11] 12] Research ....

....simulation results, and is followed by the concluding remarks of Section VII. II. THE MODEL A. The Network Model We consider a general network model based on fluid approximations. Fluid models are widely used in addressing a variety of network control problems such as congestion control [12] [5], 16] routing [5] 6] and pricing [7] 3] 17] The topology of the network is characterized by a set of nodes N 1, N and a set of links 1, L , connecting the nodes. In this network model, we make the natural assumption of connectivity, and let : 1, M denote the ....

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E. Altman and T. Basar, "Multi-user rate-based flow control," IEEE Transactions on Communications, vol. 46(7), pp. 940--949, july 1998.

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E. Altman and T. Basar, "Multiuser rate-based flow control," IEEE Trans. Commun., vol. 46, no. 7, Jul. 1998.

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E. Altman and T. Basar, "Multiuser rate-based flow control," IEEE Trans. Commun., vol. 46, no. 7, Jul. 1998.

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E. Altman and T. Bas ar, "Multiuser rate-based flow control," IEEE Trans. Commun., vol. 46, pp. 940--949, July 1998.

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